The prior art is rich in various systems and methods for data analysis, as well as various systems and methods relating to useful endeavors. In general, most existing systems and methods provide concrete functions, which have a defined response to a defined stimulus. Such systems, while embodying the “wisdom” of the designer, have a particular shortcoming in that their capabilities are static.
Intelligent or learning systems are also known. These systems are limited by the particular paradigm employed, and rarely are the learning algorithms general. In fact, while the generic theory and systems which learn are well-known, the application of such systems to particular problems requires both a detailed description of the problem, as well as knowledge of the input and output spaces. Even once these factors are known, a substantial tuning effort may be necessary to enable acceptable operation.
Therefore, the present invention builds upon the prior art, which defines various problems to be addressed, intelligent systems and methods, tuning paradigms and user interfaces. Therefore, as set forth below, and in the attached appendix of references (including abstracts), incorporated herein by reference, a significant number of references detail fundamental technologies which may be improved according to the present invention, or incorporated together to form a part of the present invention. To the some extent, these technologies are disclosed and are expressly incorporated herein by reference to avoid duplication of prior art teachings. However, the disclosure herein is not meant to be limiting as to the knowledge of a person of ordinary skill in the art. Recitation hereinbelow of these teachings or reference to these teachings is not meant to imply that the inventors hereof were necessarily in any way involved in these references, nor that the particular improvements and claimed inventions recited herein were made or conceived after the publication of these references. Thus, prior art cited herein is intended to (1) disclose information related to the application published before the filing hereof; (2) define the problem in the art to which the present invention is directed, (3) define prior art methods of solving various problems also addressed by the present invention; (4) define the state of the art with respect to methods disclosed or referenced herein; and/or (5) detail technologies used to implement methods or apparatus in accordance with the present invention.
Human Interface
Aspects of the present invention provide an advanced user interface. The subject of man-machine interfaces has been studied for many years, and indeed the entire field of ergonomics and human factors engineering revolves around optimization of human-machine interfaces. Typically, the optimization scheme optimizes the mechanical is elements of a design, or seeks to provide a universally optimized interface. Thus, a single user interface is typically provided for a system. In fact, some systems provide a variety of interfaces, for example, novice, intermediate and advanced, to provide differing balances between available control and presented complexity. Further, adaptive and/or responsive human-machine computer interfaces are now well known. However, a typical problem presented is defining a self-consistent and useful (i.e., an improvement over a well-designed static interface) theory for altering the interface. Therefore, even where, in a given application, a theory exists, the theory is typically not generalizable to other applications. Therefore, one aspect of the present invention is to provide such a theory by which adaptive and/or responsive user interfaces may be constructed and deployed.
In a particular application, the user interface according to the present invention is applied to general-purpose-type computer systems, for example, personal computers. One aspect of the present invention thus relates to a programmable device that comprises a menu-driven interface in which the user enters information using a direct manipulation input device. Such a type of interface scheme is disclosed in Verplank, William L., “Graphics in Human-Computer Communication: Principles of Graphical User-Interface Design”, Xerox Office Systems. See the references cited therein: Foley, J. D., Wallace, V. L., Chan, P., “The Human Factor of Computer Graphics Interaction Techniques”, IEEE CG&A, November 1984, pp. 13-48; Koch, H., “Ergonomische Betrachtung von Schreibtastaturen”, Humane Production, 1, pp. 12-15 (1985); Norman, D. A., Fisher, D., “Why Alphabetic Keyboards Are Not Easy To Use: Keyboard Layout Doesn't Much Matter”, Human Factors 24(5), pp. 509-519 (1982); Perspectives: High Technology 2, 1985; Knowlton, K., “Virtual Pushbuttons as a Means of Person-Machine Interaction”, Proc. of Conf. Computer Graphics, Pattern Recognition and Data Structure, Beverly Hills, Calif., May 1975, pp. 350-352; “Machine Now Reads, enters Information 25 Times Faster Than Human Keyboard Operators”, Information Display 9, p. 18 (1981); “Scanner Converts Materials to Electronic Files for PCs”, IEEE CG&A, December 1984, p. 76; “New Beetle Cursor Director Escapes All Surface Constraints”, Information Display 10, p. 12, 1984; Lu, C., “Computer Pointing Devices: Living With Mice”, High Technology, January 1984, pp. 61-65; “Finger Painting”, Information Display 12, p. 18, 1981; Kraiss, K. F., “Neuere Methoden der lnteraktion an der Schnittstelle Mensch-Maschine”, Z. F. Arbeitswissenschaft, 2, pp. 65-70, 1978; Hirzinger, G., Landzettel, K., “Sensory Feedback Structures for Robots with Supervised Learning”, IEEE Conf. on Robotics and Automation, St. Louis, March 1985; Horgan, H., “Medical Electronics”, IEEE Spectrum, January 1984, pp. 90-93.
A menu based remote control-contained display device is disclosed in Platte, Oberjatzas, and Voessing, “A New Intelligent Remote Control Unit for Consumer Electronic Device”, IEEE Transactions on Consumer Electronics, Vol. CE-31, No. 1, February 1985, 59-68.
A directional or direct manipulation-type sensor based infrared remote control is disclosed in Zeisel, Tomas, Tomaszewski, “An Interactive Menu-Driven Remote Control Unit for TV-Receivers and VC-Recorders”, IEEE Transactions on Consumer Electronics, Vol. 34, No. 3, 814-818 (1988), which relates to a control for programming with the West German Videotext system. This implementation differs from the Videotext programming system than described in Bensch, U., “VPV—VIDEOTEXT PROGRAMS VIDEORECORDER”, IEEE Transactions on Consumer Electronics, Vol. 34, No. 3, 788-792 (1988), which describes the system of Video Program System Signal Transmitters, in which the VCR is programmed by entering a code for the Video Program System signal, which is emitted by television stations in West Germany. Each separate program has a unique identifier code, transmitted at the beginning of the program, so that a user need only enter the code for the program, and the VCR will monitor the channel for the code transmission, and begin recording when the code is received, regardless of schedule changes. The Videotext Programs Recorder (VPV) disclosed does not intelligently interpret the transmission, rather the system reads the transmitted code as a literal label, without any analysis or determination of a classification of the program type.
Known manual input devices include the trackball, mouse, and joystick. In addition, other devices are known, including the so-called “J-cursor” or “mousekey” which embeds a two (x,y) or three (x,y,p) axis pressure sensor in a button conformed to a finger, present in a general purpose keyboard; a keyboard joystick of the type described in Electronic Engineering Times, Oct. 28, 1991, p. 62, “IBM Points a New Way”; a so-called “isobar” which provides a two axis input by optical sensors (θ, x), a two and one half axis (x, y, digital input) input device, such as a mouse or a “felix” device, infrared, acoustic, etc.; position sensors for determining the position of a finger or pointer on a display screen (touch-screen input) or on a touch surface, e.g., “GlidePoint” (ALPS/Cirque); goniometer input (angle position, such as human joint position detector), etc. Many of such suitable devices are summarized in Kraiss, K. F., “Alternative Input Devices For Human Computer Interaction”, Forschunginstitut Für Anthropotecahnik, Werthhoven, F. R. Germany. Another device, which may also be suitable is the GyroPoint, available from Gyration Inc., which provides 2-D or 3-D input information in up to six axes of motion: height, length, depth, roll, pitch and yaw. Such a device may be useful to assist a user in inputting a complex description of an object, by providing substantially more degrees of freedom sensing than minimally required by a standard graphic user interface. The many degrees of freedom available thus provide suitable input for various types of systems, such as “Virtual Reality” or which track a moving object, where many degrees of freedom and a high degree of input accuracy is required. The Hallpot, a device which pivots a magnet about a Hall effect sensor to produce angular orientation information, a pair of which may be used to provide information about two axes of displacement, available from Elweco, Inc, Willoughby, Ohio, may also be employed as an input device.
User input devices may be broken down into a number of categories: direct inputs, i.e. touch-screen and light pen; indirect inputs, i.e. trackball, joystick, mouse, touch-tablet, bar code scanner (see, e.g., Atkinson, Terry, “VCR Programming: Making Life Easier Using Bar Codes”), keyboard, and multi-function keys; and interactive input, i.e. Voice activation/instructions (see, e.g., Rosch, Winn L., “Voice Recognition: Understanding the Master's Voice”, PC Magazine, Oct. 27, 1987, 261-308); and eye tracker and data suit/data glove (see, e.g. Tello, Ernest R., “Between Man And Machine”, Byte, September 1988, 288-293; products of EXOS, Inc; Data Glove). Each of the aforementioned input devices has advantages and disadvantages, which are known in the art.
Studies suggest that a “direct manipulation” style of interface has advantages for menu selection tasks. This type of interface provides visual objects on a display screen, which can be manipulated by “pointing” and “clicking” on them. For example, the popular Graphical User Interfaces (“GUIs”), such as Macintosh and Microsoft Windows, and others known in the art, use a direct manipulation style interface. A device such as a touch-screen, with a more natural selection technique, is technically preferable to the direct manipulation method. However, the accuracy limitations and relatively high cost make other inputs more commercially practical. Further, for extended interactive use, touchscreens are not a panacea for office productivity applications. In addition, the user must be within arms' length of the touch-screen display. In a cursor positioning task, Albert (1982) found the trackball to be the most accurate pointing device and the touch-screen to be the least accurate when compared with other input devices such as the light pen, joystick, data tablet, trackball, and keyboard. Epps (1986) found both the mouse and trackball to be somewhat faster than both the touch-pad and joystick, but he concluded that there were no significant performance differences between the mouse and trackball as compared with the touch-pad and joystick.
It is noted that in text-based applications, an input device that is accessible, without the necessity of moving the user's hands from the keyboard, may be preferred. Thus, for example, Electronic Engineering Times (EET), Oct. 28, 1991, p. 62, discloses a miniature joystick incorporated into the functional area of the keyboard. This miniature joystick has been successfully incorporated into a number of laptop computers.
The following references are also relevant to the interface aspects of the present invention:
Hoffberg, Linda I, “AN IMPROVED HUMAN FACTORED INTERFACE FOR PROGRAMMABLE DEVICES: A CASE STUDY OF THE VCR” Master's Thesis, Tufts University (Master of Sciences in Engineering Design, November, 1990).
“Bar Code Programs VCR”, Design News, Feb. 1, 1988, 26.
“How to find the best value in VCRs”, Consumer Reports, March 1988, 135-141.
“Low-Cost VCRs: More For Less”, Consumer Reports, March 1990, 168-172.
“Nielsen Views VCRs”, Television Digest, Jun. 23, 1988, 15.
“The Highs and Lows of Nielsen Homevideo Index”, Marketing & Media Decisions, November 1985, 84-86+.
“The Quest for ‘User Friendly’”, U.S. News & World Report, Jun. 13, 1988. 54-56.
“The Smart House: Human Factors in Home Automation”, Human Factors in Practice, Dec. 1990, 1-36.
“VCR, Camcorder Trends”, Television Digest, Vol. 29:16 (Mar. 20, 1989).
“VCR's: A Look At The Top Of The Line”, Consumer Reports, March 1989, 167-170.
“VHS Videocassette Recorders”, Consumer Guide, 1990, 17-20.
Abedini, Kamran, “An Ergonomically-improved Remote Control Unit Design”, Interface '87 Proceedings, 375-380.
Abedini, Kamran, and Hadad, George, “Guidelines For Designing Better VCRs”, Report No. IME 462, Feb. 4, 1987.
Bensch, U., “VPV—VIDEOTEXT PROGRAMS VIDEORECORDER”, IEEE Transactions on Consumer Electronics, 34(3):788-792.
Berger, Ivan, “Secrets of the Universals”, Video, February 1989, 45-47+.
Beringer, D. B., “A Comparative Evaluation of Calculator Watch Data Entry Technologies: Keyboards to Chalkboards”, Applied Ergonomics, December 1985, 275-278.
Bier, E. A. et al. “MMM: A User Interface Architecture for Shared Editors on a Single Screen,” Proceedings of the ACM Symposium on User Interface Software and Technology, Nov. 11-13, 1991, p. 79.
Bishop, Edward W., and Guinness, G. Victor Jr., “Human Factors Interaction with Industrial Design”, Human Factors, 8(4):279-289 (August 1966).
Brown, Edward, “Human Factors Concepts For Management”, Proceedings of the Human Factors Society, 1973, 372-375.
Bulkeley, Debra, “The Smartest House in America”, Design News, Oct. 19, 1987, 56-61.
Card, Stuart K., “A Method for Calculating Performance times for Users of Interactive Computing Systems”, IEEE, 1979, 653-658.
Carlson, Mark A., “Design Goals for an Effective User Interface”, Electro/82 Proceedings, 3/1/1-3/1/4.
Carlson, Mark A., “Design Goals for an Effective User Interface”, Human Interfacing with Instruments, Session 3.
Carroll, Paul B., “High Tech Gear Draws Cries of “Uncle”, Wall Street Journal, Apr. 27, 1988, 29.
Cobb, Nathan, “I don't get it”, Boston Sunday Globe Magazine, Mar. 25, 1990, 23-29.
Davis, Fred, “The Great Look-and-Feel Debate”, A+, 5:9-11 (July 1987).
Dehning, Waltraud, Essig Heidrun, and Maass, Susanne, The Adaptation of Virtual Man-Computer Interfaces to User Requirements in Dialogs, Germany: Springer-Verlag, 1981.
Ehrenreich, S. L., “Computer Abbreviations—Evidence and Synthesis”, Human Factors, 27(2):143-155 (April 1985).
Friedman, M. B., “An Eye Gaze Controlled Keyboard”, Proceedings of the 2nd International Conference on Rehabilitation Engineering, 1984, 446-447.
Gilfoil, D., and Mauro, C. L., “Integrating Human Factors and Design: Matching Human Factors Methods up to Product Development”, C. L. Mauro Assoc., Inc., 1-7.
Gould, John D., Boies, Stephen J., Meluson, Antonia, Rasammy, Marwan, and Vosburgh, Ann Marie, “Entry and Selection Methods For Specifying Dates”. Human Factors, 32(2):199-214 (April 1989).
Green, Lee, “Thermo Tech: Here's a common sense guide to the new thinking thermostats”, Popular Mechanics, October 1985, 155-159.
Grudin, Jonathan, “The Case Against User Interface Consistency”, MCC Technical Report Number ACA-HI-002-89, January 1989.
Harvey, Michael G., and Rothe, James T., “VideoCassette Recorders: Their Impact on Viewers and Advertisers”, Journal of Advertising, 25:19-29 (December/January 1985).
Hawkins, William J., “Super Remotes”, Popular Science, February 1989, 76-77.
Henke, Lucy L., and Donohue, Thomas R., “Functional Displacement of Traditional TV Viewing by VCR Owners”, Journal of Advertising Research, 29:18-24 (April-May 1989).
Hoban, Phoebe, “Stacking the Decks”, New York, Feb. 16, 1987, 20:14.
Howard, Bill, “Point and Shoot Devices”, PC Magazine, 6:95-97 (August 1987).
Jane Pauley Special, NBC TV News Transcript, Jul. 17, 1990, 10:00 PM.
Kolson, Ann, “Computer wimps drown in a raging sea of technology”, The Hartford Courant, May 24, 1989, B1.
Kreifeldt, J. G., “A Methodology For Consumer Product Safety Analysis”, The 3rd National Symposium on Human Factors in Industrial Design in Consumer Products, August 1982, 175-184.
Kreifeldt, John, “Human Factors Approach to Medical Instrument Design”, Electro/82 Proceedings, 3/3/1-3/3/6.
Kuocheng, Andy Poing, and Ellingstad, Vernon S., “Touch Tablet and Touch Input”, Interface '87, 327.
Ledgard, Henry, Singer, Andrew, and Whiteside, John, Directions in Human Factors for Interactive Systems, New York, Springer-Verlag, 1981.
Lee, Eric, and MacGregor, James, “Minimizing User Search Time Menu Retrieval Systems”, Human Factors, 27(2):157-162 (April 1986).
Leon, Carol Boyd, “Selling Through the VCR”, American Demographics, December 1987, 40-43.
Long, John, “The Effect of Display Format on the Direct Entry of Numerical Information by Pointing”, Human Factors, 26(1):3-17 (February 1984).
Mantei, Marilyn M., and Teorey, Toby J., “Cost/Benefit Analysis for Incorporating Human Factors in the Software Lifecycle”, Association for Computing Machinery, 1988.
Meads, Jon A., “Friendly or Frivolous”, Datamation, Apr. 1, 1988, 98-100.
Moore, T. G. and Dartnall, “Human Factors of a Microelectronic Product: The Central Heating Timer/Programmer”, Applied Ergonomics, 1983, 13(1): 15-23.
Norman, Donald A., “Infuriating By Design”, Psychology Today, 22(3):52-56 (March 1988).
Norman, Donald A., The Psychology of Everyday Things, New York, Basic Book, Inc. 1988.
Platte, Hans-Joachim, Oberjatzas, Gunter, and Voessing, Walter, “A New Intelligent Remote Control Unit for Consumer Electronic Device”, IEEE Transactions on Consumer Electronics, Vol. CE-31(1):59-68 (February 1985).
Rogus, John G. and Armstrong, Richard, “Use of Human Engineering Standards in Design”, Human Factors, 19(1):15-23 (February 1977).
Rosch, Winn L., “Voice Recognition: Understanding the Master's Voice”, PC Magazine, Oct. 27, 1987, 261-308.
Sarver, Carleton, “A Perfect Friendship”, High Fidelity, 39:42-49 (May 1989).
Schmitt, Lee, “Let's Discuss Programmable Controllers”, Modern Machine Shop, May 1987, 90-99.
Schniederman, Ben, Designing the User Interface: Strategies for Effective Human-Computer Interaction, Reading, Mass., Addison-Wesley, 1987.
Smith, Sidney J., and Mosier, Jane N., Guidelines for Designing User Interface Software, Bedford, Mass., MITRE, 1986.
Sperling, Barbara Bied, Tullis Thomas S., “Are You a Better ‘Mouser’ or ‘Trackballer’? A Comparison of Cursor-Positioning Performance”, An Interactive/Poster Session at the CHI+GI'87 Graphics Interface and Human Factors in Computing Systems Conference.
Streeter, L. A., Ackroff, J. M., and Taylor, G. A. “On Abbreviating Command Names”, The Bell System Technical Journal, 62(6):1807-1826 (July/August 1983).
Swanson, David, and Klopfenstein, Bruce, “How to Forecast VCR Penetration”, American Demographic, December 1987, 44-45.
Tello, Ernest R., “Between Man And Machine”, Byte, September 1988, 288-293.
Thomas, John, C., and Schneider, Michael L., Human Factors in Computer Systems, New Jersey, Ablex Publ. Co., 1984.
Trachtenberg, Jeffrey A., “How do we confuse thee? Let us count the ways”, Forbes, Mar. 21, 1988, 159-160.
Tyldesley, D. A., “Employing Usability Engineering in the Development of Office Products”, The Computer Journal”, 31(5):431-436 (1988).
Verplank, William L., “Graphics in Human-Computer Communication: Principles of Graphical User-Interface Design”, Xerox Office Systems.
Voyt, Carlton F., “PLC's Learn New Languages”, Design News, Jan. 2, 1989, 78.
Whitefield, A. “Human Factors Aspects of Pointing as an Input Technique in Interactive Computer Systems”, Applied Ergonomics, June 1986, 97-104.
Wiedenbeck, Susan, Lambert, Robin, and Scholtz, Jean, “Using Protocol Analysis to Study the User Interface”, Bulletin of the American Society for Information Science, June/July 1989, 25-26.
Wilke, William, “Easy Operation of Instruments by Both Man and Machine”. Electro/82 Proceedings, 3/2/1-3/2/4.
Yoder, Stephen Kreider, “U.S. Inventors Thrive at Electronics Show”, The Wall Street Journal, Jan. 10, 1990, B1.
Zeisel, Gunter, Tomas, Philippe, Tomaszewski, Peter, “An Interactive Menu-Driven Remote Control Unit for TV-Receivers and VC-Recorders”, IEEE Transactions on Consumer Electronics, 34(3):814-818.
Agent Technologies
Presently well known human computer interfaces include so-called agent technology, in which the computer interface learns a task defined (inherently or explicitly) by the user and subsequently executes the task. Such systems are available from Firefly (www.firefly.com), and are commercially present in some on-line commerce systems, such as Amazon.com (www.amazon.com). See:
“ABIWHAP, Web Hypertext Applications Processor,” alphabase.com/abi3/whapinfo.html#profiling, (1996, Jul. 11).
“AdForce Feature Set”, www.imgis.com/index.html/core/p2—2html (1997, Apr. 11).
“IPRO,” www.ipro.com/, Internet profiles Corporation Home and other Web Pages (1996, Jul. 11).
“Media Planning is Redefined in a New Era of Online Advertising,” PR Newswire, (1996, Feb. 5).
“My Yahoo! news summary for My Yahoo! Quotes”, my.yahoo.com, (1997, Jan. 27).
“NetGravity Announces Adserver 2.1”, www.netgravity.com/news/pressrel/launch21.html (1997, Apr. 11).
“Netscape & NetGravity: Any Questions?”, www.netgravity.com/, (1996, Jul. 11).
“Network Site Main”, www.doubleclick.net/frames/general/nets2set.htm (1997, Apr. 11).
“Real Media,” www.realmedia.com/index.html, (1996, Jul. 11).
“The Front Page”, live.excite.com/?aBb (1997, Jan. 27) and (1997, Apr. 11).
“The Pointcast Network,” www.pointcast.com/, (1996, Spring).
“The Power of PenPoint”, Can et al., 1991, p. 39, Chapter 13, pp. 258-260.
“Welcome to Lycos,” www.lycos.com, (1997, Jan. 27).
Abatemarco, Fred, “From the Editor”, Popular Science, September 1992, p. 4
Berniker, M., “Nielsen plans Internet Service,” Broadcasting & Cable, 125(30):34 (1995, Jul. 24).
Berry, Deanne, et al. In an Apr. 10, 1990 news release, Symantec announced a new version of MORE (™).
Betts, M., “Sentry cuts access to naughty bits,” Computers and Security, vol. 14, No. 7, p. 615 (1995).
Boy, Guy A., Intelligent Assistant Systems, Harcourt Brace Jovanovich, 1991, uses the term “Intelligent Assistant Systems”.
Bussey, H. E., et al., “Service Architecture, Prototype Description, and Network Implications of a Personalized Information Grazing Service,” IEEE Multiple Facets of Integration Conference Proceedings, vol. 3, No. Conf. 9, Jun. 3, 1990, pp. 1046-1053.
Donnelley, J. E., “WWW media distribution via Hopewise Reliable Multicast,” Computer Networks and ISDN Systems, vol. 27, No. 6, pp. 81-788 (April, 1995).
Edwards, John R., “Q&A: Integrated Software with Macros and an Intelligent Assistant”, Byte Magazine, January 1986, vol. 11, Issue 1, pp. 120-122, critiques the Intelligent Assistant by Symantec Corporation.
Elofson, G. and Konsynski, B., “Delegation Technologies: Environmental Scanning with Intelligent Agents”, Journal of Management Information Systems, Summer 1991, vol. 8, Issue 1, pp. 37-62.
Garretson, R., “IBM Adds ‘Drawing Assistant’ Design Tool to Graphics Series”, PC Week, Aug. 13, 1985, vol. 2, Issue 32, p. 8.
Gessler, S. and Kotulla A., “PDAs as mobile WWW browsers,” Computer Networks and ISDN Systems, vol. 28, No. 1-2, pp. 53-59 (December 1995).
Glinert-Stevens, Susan, “Microsoft Publisher: Desktop Wizardry”,PC Sources, February, 1992, vol. 3, Issue 2, p. 357.
Goldberg, Cheryl, “IBM Drawing Assistant: Graphics for the EGA”, PC Magazine, Dec. 24, 1985, vol. 4, Issue 26, p. 255.
Hendrix, Gary G. and Walter, Brett A., “The Intelligent Assistant: Technical Considerations Involved in Designing Q&A's Natural-language Interface”, Byte Magazine, December 1987, vol. 12, Issue 14, p. 251.
Hoffman, D. L. et al., “A New Marketing Paradigm for Electronic Commerce,” (1996, Feb. 19), www.2000.ogsm.vanderbilt.edu novak/new.marketing.paradigm.html.
Information describing BroadVision One-to-One Application System: “Overview,” p. 1; Further Resources on One-To-One Marketing, p. 1; BroadVision Unleashes the Power of the Internet with Personalized Marketing and Selling, pp. 1-3; Frequently Asked Questions, pp. 1-3; Products, p. 1; BroadVision One-To-One(™), pp. 1-2; Dynamic Command Center, p. 1; Architecture that Scales, pp. 1-2; Technology, pp. 1; Creating a New Medium for Marketing and Selling BroadVision One-To-One and the World Wide Web a White Paper, pp. 1-15; www.broadvision.com (1996, January-March).
Jones, R., “Digital's World-Wide Web server: A case study,” Computer Networks and ISDN Systems, vol. 27, No. 2, pp. 297-306 (November 1994).
McFadden, M., “The Web and the Cookie Monster,” Digital Age, (1996, August).
Nadoli, Gajanana and Biegel, John, “Intelligent Agents in the Simulation of Manufacturing Systems”, Proceedings of the SCS Multiconference on AI and Simulation, 1989.
Nilsson, B. A., “Microsoft Publisher is an Honorable Start for DTP Beginners”, Computer Shopper, February 1992, vol. 12, Issue 2, p. 426, evaluates Microsoft Publisher and Page Wizard.
O'Connor, Rory J., “Apple Banking on Newton's Brain”, San Jose Mercury News, Wednesday, Apr. 22, 1992.
Ohsawa, I. and Yonezawa, A., “A Computational Model of an Intelligent Agent Who Talks with a Person”, Research Reports on Information Sciences, Series C, April 1989, No. 92, pp. 1-18.
Pazzani, M. et al., “Learning from hotlists and coldlists: Towards a WWW Information Filtering and Seeking Agent,” Proceedings International Conference on Tools with Artificial Intelligence, January 1995, pp. 492-495.
Poor, Alfred, “Microsoft Publisher”, PC Magazine, Nov. 26, 1991, vol. 10, Issue 20, p. 40, evaluates Microsoft Publisher.
PRNewswire, information concerning the PointCast Network (PCN) (1996, Feb. 13) p. 213.
Raggett, D., “A review of the HTML+document format,” Computer Networks and ISDN Systems, vol. 27, No. 2, pp. 35-145 (November 1994).
Rampe, Dan, et al. In a Jan. 9, 1989 news release, Claris Corporation announced two products, SmartForm Designer and SmartForm Assistant, which provide “Intelligent Assistance”, such as custom help messages, choice lists, and data-entry validation and formatting.
Ratcliffe, Mitch and Gore, Andrew, “Intelligent Agents take U.S. Bows.”, MacWeek, Mar. 2, 1992, vol. 6, No. 9, p. 1.
Sharif Heger, A. and Koen, B. V., “KNOWBOT: an Adaptive Data Base Interface”, Nuclear Science and Engineering, Feb. 1991, vol. 107, No. 2, pp. 142-157.
Soviero, Marcelle M., “Your World According to Newton”, Popular Science, September 1992, pp. 45-49.
Upendra Shardanand, “Social Information Filtering for Music Recommendation” September 1994, pp. 1-93, Massachusetts Institute of Technology, Thesis.
Weber, Thomas E., “Software Lets Marketers Target Web Ads,” The Wall Street Journal, Apr. 21, 1997
Weiman, Liza and Moran, Tom, “A Step toward the Future”, Macworld, August 1992, pp. 129-131.
Yan, T. W. and Garcia-Molina, H., “SIFT—A Tool for Wide-Area Information Dissemination,” Paper presented at the USENIX Technical Conference, New Orleans, La. (1995, January), pp. 177-186.
Industrial Controls
Industrial control systems are well known. Typically, a dedicated reliable hardware module controls a task using a conventional algorithm, with a low level user interface. These devices are programmable, and therefore a high level software program may be provided to translate user instructions into the low level commands, and to analyze any return data. See, U.S. Pat. No. 5,506,768, expressly incorporated herein by reference. See, also:
A. B. Corripio, “Tuning of Industrial Control Systems”, Instrument Society of America, Research Triangle Park, NC(1990) pp. 65-81.
C. J. Harris & S. A. Billings, “Self-Tuning and Adaptive Control: Theory and Applications”, Peter Peregrinus LTD (1981) pp. 20-33.
C. Rohrer & Clay Nesler, “Self-Tuning Using a Pattern Recognition Approach”, Johnson Controls, Inc., Research Brief 228 (Jun. 13, 1986).
D. E. Seborg, T. F. Edgar, & D. A. Mellichamp, “Process Dynamics and Control”, John Wiley & Sons, NY (1989) pp. 294-307, 538-541.
E. H. Bristol & T. W. Kraus, “Life with Pattern Adaptation”, Proceedings 1984 American Control Conference, pp. 888-892, San Diego, Calif. (1984).
Francis Schied, “Shaum's Outline Series-Theory & Problems of Numerical Analysis”, McGraw-Hill Book Co., NY(1968) pp. 236, 237, 243, 244, 261.
K. J. Astrom and B. Wittenmark, “Adaptive Control”, Addison-Wesley Publishing Company (1989) pp. 105-215.
K. J. Astrom, T. Hagglund, “Automatic Tuning of PID Controllers”, Instrument Society of America, Research Triangle Park, NC (1988) pp. 105-132.
R. W. Haines, “HVAC Systems Design Handbook”, TAB Professional and Reference Books, Blue Ridge Summit, Pa. (1988) pp. 170-177.
S. M. Pandit & S. M. Wu, “Timer Series & System Analysis with Applications”, John Wiley & Sons, Inc., NY (1983) pp. 200-205.
T. W. Kraus 7 T. J. Myron, “Self-Tuning PID Controller Uses Pattern Recognition Approach”, Control Engineering, pp. 106-111, Jun. 1984.
Pattern Recognition
Another aspect of some embodiments of the invention relates to signal analysis and complex pattern recognition. This aspect encompasses analysis of any data set presented to the system: internal, user interface, or the environment in which it operates. While semantic, optical and audio analysis systems are known, the invention is by no means limited to these types of data.
Pattern recognition involves examining a complex data set to determine similarities (in its broadest context) with other data sets, typically data sets which have been previously characterized. These data sets may comprise multivariate inputs, sequences in time or other dimension, or a combination of both multivariate data sets with multiple dimensions.
The following cited patents and publications are relevant to pattern recognition and control aspects of the present invention, and are herein expressly incorporated by reference:
U.S. Pat. No. 5,067,163, incorporated herein by reference, discloses a method for determining a desired image signal range from an image having a single background, in particular a radiation image such as a medical X-ray. This reference teaches basic image enhancement techniques.
U.S. Pat. No. 5,068,664, incorporated herein by reference, discloses a method and device for recognizing a target among a plurality of known targets, by using a probability based recognition system. This patent document cites a number of other references, which are relevant to the problem of image recognition:
Appriou, A., “Interet des theories de l'incertain en fusion de donnees”, Colloque International sur le Radar Paris, 24-28 avril 1989.
Appriou, A., “Procedure d'aide ala decision multi-informateurs. Applications ala classification multi-capteurs de cibles”, Symposium de l'Avionics Panel (AGARD) Turquie, 25-29 avril 1988.
Arrow, K. J., “Social choice and individual valves”, John Wiley and Sons Inc. (1963).
Bellman, R. E., L. A. Zadeh, “Decision making in a fuzzy environment”, Management Science, 17(4) (December 1970).
Bhatnagar, R. K., L. N. Kamal, “Handling uncertain information: a review of numeric and non-numeric methods”, Uncertainty in Artificial Intelligence, L. N. Kamal and J. F. Lemmer, Eds. (1986).
Blair, D., R. Pollack, “La logique du choix collectif” Pour la Science (1983).
Chao, J. J., E. Drakopoulos, C. C. Lee, “An evidential reasoning approach to distributed multiple hypothesis detection”, Proceedings of the 20th Conference on decision and control, Los Angeles, Calif., December 1987.
Dempster, A. P., “A generalization of Bayesian inference”, Journal of the Royal Statistical Society, Vol. 30, Series B (1968).
Dempster, A. P., “Upper and lower probabilities induced by a multivalued mapping”, Annals of mathematical Statistics, no. 38 (1967).
Dubois, D., “Modeles mathematiques de l'imprecis et de l'incertain en vue d'applications aux techniques d'aide ala decision”, Doctoral Thesis, University of Grenoble (1983).
Dubois, D., N. Prade, “Combination of uncertainty with belief functions: a reexamination”, Proceedings 9th International Joint Conference on Artificial Intelligence, Los Angeles (1985).
Dubois, D., N. Prade, “Fuzzy sets and systems-Theory and applications”, Academic Press, New York (1980).
Dubois, D., N. Prade, “Theorie des possibilites: application ala representation des connaissances en informatique”, Masson, Paris (1985).
Duda, R. O., P. E. Hart, M. J. Nilsson, “Subjective Bayesian methods for rule-based inference systems”, Technical Note 124-Artificial Intelligence Center-SRI International.
Fua, P. V., “Using probability density functions in the framework of evidential reasoning Uncertainty in knowledge based systems”, B. Bouchon, R. R. Yager, Eds. Springer Verlag (1987).
Ishizuka, M., “Inference methods based on extended Dempster and Shafer's theory for problems with uncertainty/fuzziness”, New Generation Computing, 1:159-168 (1983), Ohmsha, Ltd, and Springer Verlag.
Jeffrey, R. J., “The logic of decision”, The University of Chicago Press, Ltd., London (1983)(2nd Ed.).
Kaufmann, A., “Introduction ala theorie des sous-ensembles flous”, Vol. 1, 2 et 3-Masson-Paris (1975).
Keeney, R. L., B. Raiffa, “Decisions with multiple objectives: Preferences and value tradeoffs”, John Wiley and Sons, New York (1976).
Ksienski et al., “Low Frequency Approach to Target Identification”, Proc. of the IEEE, 63(12):1651-1660 (December 1975).
Kyburg, H. E., “Bayesian and non Bayesian evidential updating”, Artificial Intelligence 31:271-293 (1987).
Roy, B., “Classements et choix en presence de points de vue multiples”, R.I.R.O.-2eme annee-no. 8, pp. 57-75 (1968).
Roy, B., “Electre III: un algorithme de classements fonde sur une representation floue des preferences en presence de criteres multiples”, Cahiers du CERO, 20(1):3-24 (1978).
Scharlic, A., “Decider sur plusieurs criteres. Panorama de l'aide ala decision multicritere” Presses Polytechniques Romandes (1985).
Shafer, G., “A mathematical theory of evidence”, Princeton University Press, Princeton, N.J. (1976).
Sugeno, M., “Theory of fuzzy integrals and its applications”, Tokyo Institute of Technology (1974).
Vannicola et al, “Applications of Knowledge based Systems to Surveillance”, Proceedings of the 1988 IEEE National Radar Conference, 20-21 Apr. 1988, pp. 157-164.
Yager, R. R., “Entropy and specificity in a mathematical theory of Evidence”, Int. J. General Systems, 9:249-260 (1983).
Zadeh, L. A., “Fuzzy sets as a basis for a theory of possibility”, Fuzzy sets and Systems 1:3-28 (1978).
Zadeh, L. A., “Fuzzy sets”, Information and Control, 8:338-353 (1965).
Zadeh, L. A., “Probability measures of fuzzy events”, Journal of Mathematical Analysis and Applications, 23:421-427 (1968).
U.S. Pat. No. 5,067,161, incorporated herein by reference, relates to a video image pattern recognition system, which recognizes objects in near real time.
U.S. Pat. Nos. 4,817,176 and 4,802,230, both incorporated herein by reference, relate to harmonic transform methods of pattern matching of an undetermined pattern to known patterns, and are useful in the pattern recognition method of the present invention. U.S. Pat. No. 4,998,286, incorporated herein by reference, relates to a harmonic transform method for comparing multidimensional images, such as color images, and is useful in the present pattern recognition methods.
U.S. Pat. No. 5,067,166, incorporated herein by reference, relates to a pattern recognition system, in which a local optimum match between subsets of candidate reference label sequences and candidate templates. It is clear that this method is useful in the pattern recognition aspects of the present invention. It is also clear that the interface and control system of the present invention are useful adjuncts to the method disclosed in U.S. Pat. No. 5,067,166.
U.S. Pat. No. 5,048,095, incorporated herein by reference, relates to the use of a genetic learning algorithm to adaptively segment images, which is an initial stage in image recognition. This patent has a software listing for this method. It is clear that this method is useful in the pattern recognition aspects of the present invention. It is also clear that the interface and control system of the present invention are useful adjuncts to the method disclosed in U.S. Pat. No. 5,048,095.
Fractal-Based Image Processing
Fractals are a relatively new field of science and technology that relate to the study of order and chaos. While the field of fractals is now very dense, a number of relevant principles are applicable. First, when the coordinate axes of a space are not independent, and are related by a recursive algorithm, then the space is considered to have a fractional dimensionality. One characteristic of such systems is that a mapping of such spaces tends to have self-similarity on a number of scales. Interestingly, natural systems have also been observed to have self-similarity over several orders of magnitude, although as presently believed, not over an unlimited range of scales. Therefore, one theory holds that images of natural objects may be efficiently described by iterated function systems (IFS), which provide a series of parameters for a generic formula or algorithm, which, when the process is reversed, is visually similar to the starting image. Since the “noise” of the expanded data is masked by the “natural” appearance of the result, visually acceptable image compression may be provided at relatively high compression ratios. This theory remains the subject of significant debate, and, for example, wavelet algorithm advocates claim superior results for a more general set of starting images. It is noted that, on a mathematical level, wavelets and fractal theories have some common threads.
According to a particular embodiment of the invention, the expression of an image as an ordered set of coefficients of an algorithm, wherein the coefficients relate to elements of defined variation in scale, and the resulting set of coefficients is related to the underlying image morphology, is exploited in order to provide a means for pattern analysis and recognition without requiring decompression to an orthogonal coordinate space.
U.S. Pat. Nos. 5,065,447, and 4,941,193, both incorporated herein by reference, relate to the compression of image data by using fractal transforms. These are discussed in detail below. U.S. Pat. No. 5,065,447 cites a number of references, relevant to the use of fractals in image processing:
U.S. Pat. No. 4,831,659.
“A New Class of Markov Processes for Image Encoding”, School of Mathematics, Georgia Inst. of Technology (1988), pp. 14-32.
“Construction of Fractal Objects with Iterated Function Systems”, Siggraph '85 Proceedings, 19(3):271-278 (1985).
“Data Compression: Pntng by Numbrs”, The Economist, May 21, 1988.
“Fractal Geometry—Understanding Chaos”, Georgia Tech Alumni Magazine, p. 16 (Spring 1986).
“Fractal Modelling of Biological Structures”, Perspectives in Biological Dynamics and Theoretical Medicine, Koslow, Mandell, Shlesinger, eds., Annals of New York Academy of Sciences, vol. 504, 179-194 (date unknown).
“Fractal Modelling of Real World Images, Lecture Notes for Fractals: Introduction, Basics and Perspectives”, Siggraph (1987).
“Fractals—A Geometry of Nature”, Georgia Institute of Technology Research Horizons, p. 9 (Spring 1986).
A. Jacquin, “A Fractal Theory of Iterated Markov Operators with Applications to Digital Image Coding”, PhD Thesis, Georgia Tech, 1989.
A. Jacquin, “Image Coding Based on a Fractal Theory of Iterated Contractive Image Transformations” p. 18, Jan. 1992 (Vol 1 Issue 1) of IEEE Trans on Image Processing.
A. Jacquin, ‘Fractal image coding based on a theory of iterated contractive image transformations’, Proc. SPIE Visual Communications and Image Processing, 1990, pages 227-239.
A. E. Jacquin, ‘A novel fractal block-coding technique for digital images’, Proc. ICASSP 1990.
Baldwin, William, “Just the Bare Facts, Please”, Forbes Magazine, Dec. 12, 1988.
Barnsley et al., “A Better Way to Compress Images”, Byte Magazine, January 1988, pp. 213-225.
Barnsley et al., “Chaotic Compression”, Computer Graphics World, November 1987.
Barnsley et al., “Harnessing Chaos For Images Synthesis”, Computer Graphics, 22(4):131-140 (August, 1988).
Barnsley et al., “Hidden Variable Fractal Interpolation Functions”, School of Mathematics, Georgia Institute of Technology, Atlanta, Ga. 30332, July, 1986.
Barnsley, M. F., “Fractals Everywhere”, Academic Press, Boston, Mass., 1988.
Barnsley, M. F., and Demko, S., “Iterated Function Systems and The Global Construction of Fractals”, Proc. R. Soc. Lond., A399:243-275 (1985).
Barnsley, M. F., Ervin, V., Hardin, D., Lancaster, J., “Solution of an Inverse Problem for Fractals and Other Sets”, Proc. Natl. Acad. Sci. U.S.A., 83:1975-1977 (April 1986).
Beaumont J M, “Image data compression using fractal techniques”, British Telecom Technological Journal 9(4):93-108 (1991).
Byte Magazine, January 1988, supra, cites:
D. S. Mazel, Fractal Modeling of Time-Series Data, PhD Thesis, Georgia Tech, 1991. (One dimensional, not pictures).
Derra, Skip, “Researchers Use Fractal Geometry”, Research and Development Magazine, March 1988.
Elton, J., “An Ergodic Theorem for Iterated Maps”, Journal of Ergodic Theory and Dynamical Systems, 7 (1987).
Fisher Y, “Fractal image compression”, Siggraph 92.
Fractal Image Compression Michael F. Barnsley and Lyman P. Hurd ISBN 0-86720-457-5, ca. 250 pp.
Fractal Image Compression: Theory and Application, Yuval Fisher (ed.), Springer Verlag, New York, 1995. ISBN number 0-387-94211-4.
Fractal Modelling of Biological Structures, School of Mathematics, Georgia Institute of Technology (date unknown).
G. E. Oien, S. Lepsoy & T. A. Ramstad, ‘An inner product space approach to image coding by contractive transformations’, Proc. ICASSP 1991, pp 2773-2776.
Gleick, James, “Making a New Science”, pp. 215, 239, date unknown.
Graf S, “Barnsley's Scheme for the Fractal Encoding of Images”, Journal Of Complexity, V8, 72-78 (1992).
Jacobs, E. W., Y. Fisher and R. D. Boss. “Image Compression: A study of the Iterated Transform Method. Signal Processing 29, (1992)25-263.
M. Barnsley, L. Anson, “Graphics Compression Technology, SunWorld, October 1991, pp. 42-52.
M. F. Barnsley, A. Jacquin, F. Malassenet, L. Reuter & A. D. Sloan, ‘Harnessing chaos for image synthesis’, Computer Graphics, vol 22 no 4 pp 131-140, 1988.
M. F. Barnsley, A. E. Jacquin, ‘Application of recurrent iterated function systems to images’, Visual Comm. and Image Processing, vol SPIE-1001, 1988.
Mandelbrot, B., “The Fractal Geometry of Nature”, W.H. Freeman & Co., San Francisco, Calif., 1982, 1977.
Monro D M and Dudbridge F, “Fractal block coding of images”, Electronics Letters 28(11):1053-1054 (1992).
Monro D. M. & Dudbridge F. ‘Fractal approximation of image blocks’, Proc ICASSP 92, pp. III: 485-488.
Monro D. M. ‘A hybrid fractal transform’, Proc ICASSP 93, pp. V: 169-72.
Monro D. M., Wilson D., Nicholls J. A. ‘High speed image coding with the Bath Fractal Transform’, IEEE International Symposium on Multimedia Technologies Southampton, April 1993.
Peterson, Ivars, “Packing It In—Fractals.”, Science News, 131(18):283-285 (May 2, 1987).
S. A. Hollatz, “Digital image compression with two-dimensional affine fractal interpolation functions”, Department of Mathematics and Statistics, University of Minnesota-Duluth, Technical Report 91-2. (a nuts-and-bolts how-to-do-it paper on the technique).
Stark, J., “Iterated function systems as neural networks”, Neural Networks, Vol 4, pp 679-690, Pergamon Press, 1991.
Vrscay, Edward R. “Iterated Function Systems: Theory, Applications, and the Inverse Problem. Fractal Geometry and Analysis, J. Belair and S. Dubuc (eds.) Kluwer Academic, 1991. 405-468.
U.S. Pat. No. 5,347,600, incorporated herein by reference, relates to a method and apparatus for compression and decompression of digital image data, using fractal methods. According to this method, digital image data is automatically processed by dividing stored image data into domain blocks and range blocks. The range blocks are subjected to processes such as a shrinking process to obtain mapped range blocks. The range blocks or domain blocks may also be processed by processes such as affine transforms. Then, for each domain block, the mapped range block which is most similar to the domain block is determined, and the address of that range block and the processes the blocks were subjected to are combined as an identifier which is appended to a list of identifiers for other domain blocks. The list of identifiers for all domain blocks is called a fractal transform and constitutes a compressed representation of the input image. To decompress the fractal transform and recover the input image, an arbitrary input image is formed into range blocks and the range blocks processed in a manner specified by the identifiers to form a representation of the original input image.
“Image Compression Using Fractals and Wavelets”, Final Report for the Phase II Contract Sponsored by the Office of Naval Research, Contract No. N00014-91-C-0117, Netrologic Inc., San Diego, Calif. (Jun. 2, 1993), relates to various methods of compressing image data, including fractals and wavelets. This method may also be applicable in pattern recognition applications. This reference provides theory and comparative analysis of compression schemes.
A fractal-processing method based image extraction method is described in Kim, D. H.; Caulfield, H. J.; Jannson, T.; Kostrzewski, A.; Savant, G, “Optical fractal image processor for noise-embedded targets detection”, Proceedings of the SPIE-The International Society for Optical Engineering, Vol. 2026, p. 144-9 (1993) (SPIE Conf: Photonics for Processors, Neural Networks, and Memories 12-15 Jul. 1993, San Diego, Calif., USA). According to this paper, a fractal dimensionality measurement and analysis-based automatic target recognition (ATR) is described. The ATR is a multi-step procedure, based on fractal image processing, and can simultaneously perform preprocessing, interest locating, segmenting, feature extracting, and classifying. See also, Cheong, C. K.; Aizawa, K.; Saito, T.; Hatori, M., “Adaptive edge detection with fractal dimension”, Transactions of the Institute of Electronics, Information and Communication Engineers D-II, J76D-II(11):2459-63 (1993); Hayes, H. I.; Solka, J. L.; Priebe, C. E.; “Parallel computation of fractal dimension”, Proceedings of the SPIE-The International Society for Optical Engineering, 1962:219-30 (1993); Priebe, C. E.; Solka, J. L.; Rogers, G. W., “Discriminant analysis in aerial images using fractal based features”, Proceedings of the SPIE-The International Society for Optical Engineering, 1962:196-208 (1993). See also, Anson, L., “Fractal Image Compression”, Byte, October 1993, pp. 195-202; “Fractal Compression Goes On-Line”, Byte, September 1993.
Methods employing other than fractal-based algorithms may also be used. See, e.g., Liu, Y., “Pattern recognition using Hilbert space”, Proceedings of the SPIE-The International Society for Optical Engineering, 1825:63-77 (1992), which describes a learning approach, the Hilbert learning. This approach is similar to Fractal learning, but the Fractal part is replaced by Hilbert space. Like the Fractal learning, the first stage is to encode an image to a small vector in the internal space of a learning system. The next stage is to quantize the internal parameter space. The internal space of a Hilbert learning system is defined as follows: a pattern can be interpreted as a representation of a vector in a Hilbert space. Any vectors in a Hilbert space can be expanded. If a vector happens to be in a subspace of a Hilbert space where the dimension L of the subspace is low (order of 10), the vector can be specified by its norm, an L-vector, and the Hermitian operator which spans the Hilbert space, establishing a mapping from an image space to the internal space P. This mapping converts an input image to a 4-tuple: tin P=(Norm, T, N, L-vector), where T is an operator parameter space, N is a set of integers which specifies the boundary condition. The encoding is implemented by mapping an input pattern into a point in its internal space. The system uses local search algorithm, i.e., the system adjusts its internal data locally. The search is first conducted for an operator in a parameter space of operators, then an error function delta (t) is computed. The algorithm stops at a local minimum of delta (t). Finally, the input training set divides the internal space by a quantization procedure. See also, Liu, Y., “Extensions of fractal theory”, Proceedings of the SPIE-The International Society for Optical Engineering, 1966:255-68 (1993).
Fractal methods may be used for pattern recognition. See, Sadjadi, F., “Experiments in the use of fractal in computer pattern recognition”, Proceedings of the SPIE-The International Society for Optical Engineering, 1960:214-22 (1993). According to this reference, man-made objects in infrared and millimeterwave (MMW) radar imagery may be recognized using fractal-based methods. The technique is based on estimation of the fractal dimensions of sequential blocks of an image of a scene and slicing of the histogram of the fractal dimensions computed by Fourier regression. The technique is shown to be effective for the detection of tactical military vehicles in I R, and of airport attributes in MMW radar imagery.
In addition to spatial self-similarity, temporal self-similarity may also be analyzed using fractal methods. See, Reusens, E., “Sequence coding based on the fractal theory of iterated transformations systems”, Proceedings of the SPIE-The International Society for Optical Engineering, 2094(pt.1):132-40 (1993). This reference describes a scheme based on the iterated functions systems theory which relies on a 3D approach in which the sequence is adaptively partitioned. Each partition block can be coded either by using the spatial self similarities or by exploiting temporal redundancies.
Fractal compression methods may be used for video data for transmission. See, Hurtgen, B.; Buttgen, P., “Fractal approach to low rate video coding”, Proceedings of the SPIE-The International Society for Optical Engineering, 2094(pt.1):120-31 (1993). This reference relates to a method for fast encoding and decoding of image sequences on the basis of fractal coding theory and the hybrid coding concept. The DPCM-loop accounts for statistical dependencies of natural image sequences in the temporal direction. Those regions of the original image where the prediction, i.e. motion estimation and compensation, fails are encoded using an advanced fractal coding scheme, suitable for still images, and whose introduction instead of the commonly used Discrete Cosine Transform (DCT)-based coding is advantageous especially at very low bit rates (8-64 kbit/s). In order to increase reconstruction quality, encoding speed and compression ratio, some additional features such as hierarchical codebook search and multilevel block segmentation may be employed. This hybrid technique may be used in conjunction with the present adaptive interface or other features of the present invention.
Fractal methods may be used to segment an image into objects having various surface textures. See, Zhi-Yan Xie; Brady, M., “Fractal dimension image for texture segmentation”, ICARCV '92. Second International Conference on Automation, Robotics and Computer Vision, p. CV-4.3/1-5 vol. 1, (1992). According to this reference, the fractal dimension and its change over boundaries of different homogeneous textured regions is analyzed and used to segment textures in infrared aerial images. Based on the fractal dimension, different textures map into different fractal dimension image features, such that there is smooth variation within a single homogeneous texture but sharp variation at texture boundaries. Since the fractal dimension remains unchanged under linear transformation, this method is robust for dismissing effects caused by lighting and other extrinsic factors. Morphology is the only tool used in the implementation of the whole process: texture feature extraction, texture segmentation and boundary detection. This makes possible parallel implementations of each stage of the process.
Rahmati, M.; Hassebrook, L. G., “Intensity- and distortion-invariant pattern recognition with complex linear morphology”, Pattern Recognition, 27 (4):549-68 (1994) relates to a unified model based pattern recognition approach is introduced which can be formulated into a variety of techniques to be used for a variety of applications. In this approach, complex phasor addition and cancellation are incorporated into the design of filter(s) to perform implicit logical operations using linear correlation operators. These implicit logical operations are suitable to implement high level gray scale morphological transformations of input images. In this way non-linear decision boundaries are effectively projected into the input signal space yet the mathematical simplicity of linear filter designs is maintained. This approach is applied to the automatic distortion- and intensity-invariant object recognition problem. A set of shape operators or complex filters is introduced which are logically structured into a filter bank architecture to accomplish the distortion and intensity-invariant system. This synthesized complex filter bank is optimally sensitive to fractal noise representing natural scenery. The sensitivity is optimized for a specific fractal parameter range using the Fisher discriminant. The output responses of the proposed system are shown for target, clutter, and pseudo-target inputs to represent its discrimination and generalization capability in the presence of distortion and intensity variations. Its performance is demonstrated with realistic scenery as well as synthesized inputs.
Sprinzak, J.; Werman, M., “Affine point matching”, Pattern Recognition Letters, 15(4):337-9 (1994), relates to a pattern recognition method. A fundamental problem of pattern recognition, in general, is recognizing and locating objects within a given scene. The image of an object may have been distorted by different geometric transformations such as translation, rotation, scaling, general affine transformation or perspective projection. The recognition task involves finding a transformation that superimposes the model on its instance in the image. This reference proposes an improved method of superimposing the model.
Temporal Image Analysis
Temporal image analysis is a well known field. This field holds substantial interest at present for two reasons. First, by temporal analysis of a series of two dimensional images, objects and object planes may be defined, which provide basis for efficient yet general algorithms for video compression, such as the Motion Picture Experts Group (MPEG) series of standards. Second, temporal analysis has applications in signal analysis for an understanding and analysis of the signal itself.
U.S. Pat. No. 5,280,530, incorporated herein by reference, relates to a method and apparatus for tracking a moving object in a scene, for example the face of a person in videophone applications, comprises forming an initial template of the face, extracting a mask outlining the face, dividing the template into a plurality (for example sixteen) sub-templates, searching the next frame to find a match with the template, searching the next frame to find a match with each of the sub-templates, determining the displacements of each of the sub-templates with respect to the template, using the displacements to determine affine transform coefficients and performing an affine transform to produce an updated template and updated mask.
U.S. Pat. No. 5,214,504 relates to a moving video image estimation system, based on an original video image of time n and time n+1, the centroid, the principal axis of inertia, the moment about the principal axis of inertia and the moment about the axis perpendicular to the principal axis of inertia are obtained. By using this information, an affine transformation for transforming the original video image at time n to the original video image at time n+1 is obtained. Based on the infinitesimal transformation (A), {eAt, and eA(t-1)} obtained by making the affine transformation continuous with regard to time is executed on the original video image at time n and time n+1. The results are synthesized to perform an interpolation between the frames. {eA(t-1)} is applied to the original video system time n+1. The video image after time n+1 is thereby protected.
U.S. Pat. No. 5,063,603, incorporated herein by reference, relates to a dynamic method for recognizing objects and image processing system therefor. This reference discloses a method of distinguishing between different members of a class of images, such as human beings. A time series of successive relatively high-resolution frames of image data, any frame of which may or may not include a graphical representation of one or more predetermined specific members (e.g., particular known persons) of a given generic class (e.g. human beings), is examined in order to recognize the identity of a specific member; if that member's image is included in the time series. The frames of image data may be examined in real time at various resolutions, starting with a relatively low resolution, to detect whether some earlier-occurring frame includes any of a group of image features possessed by an image of a member of the given class. The image location of a detected image feature is stored and then used in a later-occurring, higher resolution frame to direct the examination only to the image region of the stored location in order to (1) verify the detection of the aforesaid image feature, and (2) detect one or more other of the group of image features, if any is present in that image region of the frame being examined. By repeating this type of examination for later and later occurring frames, the accumulated detected features can first reliably recognize the detected image region to be an image of a generic object of the given class, and later can reliably recognize the detected image region to be an image of a certain specific member of the given class. Thus, a human identity recognition feature of the present invention may be implemented in this manner. Further, it is clear that this recognition feature may form an integral part of certain embodiments of the present invention. It is also clear that the various features of the present invention would be applicable as an adjunct to the various elements of the system disclosed in U.S. Pat. No. 5,063,603.
U.S. Pat. No. 5,067,160, incorporated herein by reference, relates to a motion-pattern recognition apparatus, having adaptive capabilities. The apparatus recognizes a motion of an object that is moving and is hidden in an image signal, and discriminates the object from the background within the signal. The apparatus has an image-forming unit comprising non-linear oscillators, which forms an image of the motion of the object in accordance with an adjacent-mutual-interference-rule, on the basis of the image signal. A memory unit, comprising non-linear oscillators, stores conceptualized meanings of several motions. A retrieval unit retrieves a conceptualized meaning close to the motion image of the object. An altering unit alters the rule, on the basis of the conceptualized meaning. The image forming unit, memory unit, retrieval unit and altering unit form a holonic-loop. Successive alterations of the rules by the altering unit within the holonic loop change an ambiguous image formed in the image forming unit into a distinct image. U.S. Pat. No. 5,067,160 cites the following references, which are relevant to the task of discriminating a moving object in a background:
U.S. Pat. No. 4,710,964.
Shimizu et al, “Principle of Holonic Computer and Holovision”, Journal of the Institute of Electronics, Information and Communication, 70(9):921-930 (1987).
Omata et al, “Holonic Model of Motion Perception”, IEICE Technical Reports, Mar. 26, 1988, pp. 339-346.
Ohsuga et al, “Entrainment of Two Coupled van der Pol Oscillators by an External Oscillation”, Biological Cybernetics, 51:225-239 (1985).
U.S. Pat. No. 5,065,440, incorporated herein by reference, relates to a pattern recognition apparatus, which compensates for, and is thus insensitive to pattern shifting, thus being useful for decomposing an image or sequence of images, into various structural features and recognizing the features. U.S. Pat. No. 5,065,440 cites the following references, incorporated herein by reference, which are also relevant to the present invention: U.S. Pat. Nos. 4,543,660, 4,630,308, 4,677,680, 4,809,341, 4,864,629, 4,872,024 and 4,905,296.
Recent analyses of fractal image compression techniques have tended to imply that, other than in special circumstances, other image compression methods are “better” than a Barnsley-type image compression system, due to the poor performance of compression processors and lower than expected compression ratios. Further, statements attributed to Barnsley have indicated that the Barnsley technique is not truly a “fractal” technique, but rather a vector quantization process which employs a recursive library. Nevertheless, these techniques and analyses have their advantages. As stated hereinbelow, the fact that the codes representing the compressed image are hierarchical represents a particular facet exploited by the present invention.
Another factor which makes fractal methods and analysis relevant to the present invention is the theoretical relation to optical image processing and holography. Thus, while such optical systems may presently be cumbersome and economically unfeasible, and their implementation in software models slow, these techniques nevertheless hold promise and present distinct advantages.
Biometric Analysis
Biometric analysis comprises the study of the differences between various organisms, typically of the same species. Thus, the intraspecies variations become the basis for differentiation and identification. In practice, there are many applications for biometric analysis systems, for example in security applications, these allow identification of a particular human.
U.S. Pat. No. 5,055,658, incorporated herein by reference, relates to a security system employing digitized personal characteristics, such as voice. The following references are cited:
“Voice Recognition and Speech Processing”, Elektor Electronics, September 1985, pp. 56-57.
Naik et al., “High Performance Speaker Verification.”, ICASSP 86, Tokyo, CH2243-4/86/0000-0881, IEEE 1986, pp. 881-884.
Shinan et al., “The Effects of Voice Disguise.”, ICASSP 86, Tokyo, CH2243-4/86/0000-0885, IEEE 1986, pp. 885-888.
Parts of this system relating to speaker recognition may be used to implement a voice recognition system of the present invention for determining an actor or performer in a broadcast.
Neural Networks
Neural networks are a particular type of data analysis tool. There are characterized by the fact that the network is represented by a set of “weights”, which are typically scalar values, which are derived by a formula which is designed to reduce the error between a data pattern representing a known state and the network's prediction of that state. These networks, when provided with sufficient complexity and an appropriate training set, may be quite sensitive and precise. Further, the data pattern may be arbitrarily complex (although the computing power required to evaluate the output will also grow) and therefore these systems may be employed for video and other complex pattern analysis.
U.S. Pat. No. 5,067,164, incorporated herein by reference, relates to a hierarchical constrained automatic learning neural network for character recognition, and thus represents an example of a trainable neural network for pattern recognition, which discloses methods which are useful for the present invention. This Patent cites various references of interest:
U.S. Pat. Nos. 4,760,604, 4,774,677 and 4,897,811.
LeCun, Y., Connectionism in Perspective, R. Pfeifer, Z. Schreter, F. Fogelman, L. Steels, (Eds.), 1989, “Generalization and Network Design Strategies”, pp. 143-55.
LeCun, Y., et al., “Handwritten Digit Recognition: Applications of Neural.”, IEEE Comm. Magazine, pp. 41-46 (November 1989).
Lippmann, R. P., “An Introduction to Computing with Neural Nets”, IEEE ASSP Magazine, 4(2):4-22 (April 1987).
Rumelhart, D. E., et al., Parallel Distr. Proc.: Explorations in Microstructure of Cognition, vol. 1, 1986, “Learning Internal Representations by Error Propagation”, pp. 318-362.
U.S. Pat. Nos. 5,048,100, 5,063,601 and 5,060,278, all incorporated herein by reference, also relate to neural network adaptive pattern recognition methods and apparatuses. It is clear that the methods of 5,048,100, 5,060,278 and 5,063,601 may be used to perform the adaptive pattern recognition functions of the present invention. More general neural networks are disclosed in U.S. Pat. Nos. 5,040,134 and 5,058,184, both incorporated herein be reference, which provide background on the use of neural networks. In particular, 5,058,184 relates to the use of the apparatus in information processing and feature detection applications.
U.S. Pat. No. 5,058,180, incorporated herein by reference, relates to neural network apparatus and method for pattern recognition, and is thus relevant to the intelligent pattern recognition functions of the present invention. This patent cites the following documents of interest:
U.S. Pat. Nos. 4,876,731 and 4,914,708.
Carpenter, G. A., S. Grossberg, “The Art of Adaptive Pattern Recognition by a Self-Organizing Neural Network,” IEEE Computer, March 1988, pp. 77-88.
Computer Visions, Graphics, and Image Processing 1987, 37:54-115.
Grossberg, S., G. Carpenter, “A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine,” Computer Vision, Graphics, and Image Processing (1987, 37, 54-115), pp. 252-315.
Gullichsen E., E. Chang, “Pattern Classification by Neural Network: An Experiment System for Icon Recognition,” ICNN Proceeding on Neural Networks, March 1987, pp. IV-725-32.
Jackel, L. D., H. P. Graf, J. S. Denker, D. Henderson and I. Guyon, “An Application of Neural Net Chips: Handwritten Digit Recognition,” ICNN Proceeding, 1988, pp. II-107-15.
Lippman, R. P., “An Introduction to Computing with Neural Nets,” IEEE ASSP Magazine, April 1987, pp. 4-22.
Pawlicki, T. F., D. S. Lee, J. J. Hull and S. N. Srihari, “Neural Network Models and their Application to Handwritten Digit Recognition,” ICNN Proceeding, 1988, pp. II-63-70.
Chao, T.-H.; Hegblom, E.; Lau, B.; Stoner, W. W.; Miceli, W. J., “Optoelectronically implemented neural network with a wavelet preprocessor”, Proceedings of the SPIE-The International Society for Optical Engineering, 2026:472-82 (1993), relates to an optoelectronic neural network based upon the Neocognitron paradigm which has been implemented and successfully demonstrated for automatic target recognition for both focal plane array imageries and range-Doppler radar signatures. A particular feature of this neural network architectural design is the use of a shift-invariant multichannel Fourier optical correlation as a building block for iterative multilayer processing. A bipolar neural weights holographic synthesis technique was utilized to implement both the excitatory and inhibitory neural functions and increase its discrimination capability. In order to further increase the optoelectronic Neocognitron's self-organization processing ability, a wavelet preprocessor was employed for feature extraction preprocessing (orientation, size, location, etc.). A multichannel optoelectronic wavelet processor using an e-beam complex-valued wavelet filter is also described.
Neural networks are important tools for extracting patterns from complex input sets. These systems do not require human comprehension of the pattern in order to be useful, although human understanding of the nature of the problem is helpful in designing the neural network system, as is known in the art. Feedback to the neural network is integral to the training process. Thus, a set of inputs is mapped to a desired output range, with the network minimizing an “error” for the training data set. Neural networks may differ based on the computation of the “error”, the optimization process, the method of altering the network to minimize the error, and the internal topology. Such factors are known in the art.
Optical Pattern Recognition
Optical image processing holds a number of advantages. First, images are typically optical by their nature, and therefore processing by this means may (but not always) avoid a data conversion. Second, many optical image processing schemes are inherently or easily performed in parallel, improving throughput. Third, optical circuits typically have response times shorter than electronic circuits, allowing potentially short cycle times. While many optical phenomena may be modeled using electronic computers, appropriate applications for optical computing, such as pattern recognition, hold promise for high speed in systems of acceptable complexity.
U.S. Pat. No. 5,060,282, incorporated herein by reference, relates to an optical pattern recognition architecture implementing the mean-square error correlation algorithm. This method allows an optical computing function to perform pattern recognition functions. U.S. Pat. No. 5,060,282 cites the following references, which are relevant to optical pattern recognition:
Kellman, P., “Time Integrating Optical Signal Processing”, Ph.D. Dissertation, Stanford University, 1979, pp. 51-55.
Molley, P., “Implementing the Difference-Squared Error Algorithm Using An Acousto-Optic Processor”, SPIE, 1098:232-239, (1989).
Molley, P., et al., “A High Dynamic Range Acousto-Optic Image Correlator for Real-Time Pattern Recognition”, SPIE, 938:55-65 (1988).
Psaltis, D., “Incoherent Electro-Optic Image Correlator”, Optical Engineering, 23(1):12-15 (January/February 1984).
Psaltis, D., “Two-Dimensional Optical Processing Using One-Dimensional Input Devices”, Proceedings of the IEEE, 72(7):962-974 (July 1984).
Rhodes, W., “Acousto-Optic Signal Processing: Convolution and Correlation”, Proc. of the IEEE, 69(1):65-79 (January 1981).
Vander Lugt, A., “Signal Detection By Complex Spatial Filtering”, IEEE Transactions On Information Theory, IT-10, 2:139-145 (April 1964).
U.S. Pat. Nos. 5,159,474 and 5,063,602, expressly incorporated herein by reference, also relate to optical image correlators. Also of interest is Li, H. Y., Y. Qiao and D. Psaltis, Applied Optics (April, 1993). See also, Bains, S., “Trained Neural Network Recognizes Faces”, Laser Focus World, June, 1993, pp. 26-28; Bagley, H. & Sloan, J., “Optical Processing: Ready For Machine Vision?”, Photonics Spectra, August 1993, pp. 101-106.
Optical pattern recognition has been especially applied to two dimensional patterns. In an optical pattern recognition system, an image is correlated with a set of known image patterns represented on a hologram, and the product is a pattern according to a correlation between the input pattern and the provided known patterns. Because this is an optical technique, it is performed nearly instantaneously, and the output information can be reentered into an electronic digital computer through optical transducers known in the art. Such a system is described in Casasent, D., Photonics Spectra, November 1991, pp. 134-140. The references cited therein provide further details of the theory and practice of such a system: Lendaris, G. G., and Stanely, G. L., “Diffraction Pattern Sampling for Automatic Target Recognition”, Proc. IEEE 58:198-205 (1979); Ballard, D. H., and Brown, C. M., Computer Vision, Prentice Hall, Englewood Cliffs, N.J. (1982); Optical Engineering 28:5 (May 1988)(Special Issue on product inspection); Richards J., and Casasent, D., “Real Time Hough Transform for Industrial Inspection” Proc. SPIE Technical Symposium, Boston 19891192:2-21 (1989); Maragos, P., “Tutorial Advances in Morphological Image Processing” Optical Engineering 26:7:623-632 (1987); Casasent, D., and Tescher, A., Eds., “Hybrid Image and Signal Processing II”, Proc. SPIE Technical Symposium, April 1990, Orlando Fla. 1297 (1990); Ravichandran, G. and Casasent, D., “Noise and Discrimination Performance of the MINACE Optical Correlation Filter”, Proc. SPIE Technical Symposium, April 1990, Orlando Fla., 1471 (1990); Weshsler, H. Ed., “Neural Nets For Human and Machine Perception”, Academic Press, New York (1991).
By employing volume holographic images, the same types of paradigms may be applied to three dimensional images.
Query by Image Content
Query by image content, a phrase coined by IBM researchers, relates to a system for retrieving image data stored in a database on the basis of the colors, textures, morphology or objects contained within the image. Therefore, the system characterizes the stored images to generate a metadata index, which can then be searched. Unindexed searching is also possible.
A number of query by image content systems are known, including both still and moving image systems, for example from IBM (QBIC), Apple (Photobook), Belmont Research Inc. (Steve Gallant), BrainTech Inc.; Center for Intelligent Information Retrieval (Umass Amherst), Virage, Inc., Informix Software, Inc. (Illustra), Islip Media, Inc., Magnifi, Numinous Technologies, Columbia University VisualSeek/WebSeek (Chang et al., John R. Smith), Monet (CWI and UvA), Visual Computing Laboratory, UC San Diego (ImageGREP, White and Jain). See also, ISO/IEC MPEG-7 literature.
See, Jacobs, et al., “Fast Multiresolution Image Querying”, Department of Computer Science, University of Washington, Seattle Wash.
U.S. Pat. No. 5,655,117, expressly incorporated herein by reference, relates to a method and apparatus for indexing multimedia information streams for content-based retrieval. See also:
Gong et al, “An Image Database System with Content Capturing and Fast Image Indexing Abilities”, PROC of the International Conference on Multimedia Computing and Systems, pp. 121-130 May 19, 1994.
Hongjiang, et al., Digital Libraries, “A Video Database System for Digital Libraries”, pp. 253-264, May 1994.
S. Abe and Y. Tonomura, Systems and Computers in Japan, vol. 24, No. 7, “Scene Retrieval Method Using Temporal Condition Changes”, pp. 92-101,1993.
Salomon et al, “Using Guides to Explore Multimedia Databases”, PROC of the Twenty-Second Annual Hawaii International Conference on System Sciences. vol. IV, 3-6 Jan. 1989, pp. 3-12 vol. 4. Jan. 6, 1989.
Stevens, “Next Generation Network and Operating System Requirements for Continuous Time Media”, in Herrtwich (Ed.), Network and Operating System Support for Digital Audio and Video, pp. 197-208, November 1991.
U.S. Pat. No. 5,606,655, expressly incorporated herein by reference, relates to a method for representing contents of a single video shot using frames. The method provides a representative frame (Rframe) for a group of frames in a video sequence, selecting a reference frame from the group of frames and storing the reference frame in a computer memory. This system defines a peripheral motion tracking region along an edge of the reference frame and successively tracks movement of boundary pixels in the tracking region, symbolizing any of the length of the shot and the presence of any caption. See, also:
“A Magnifier Tool for Video Data”, Mills et al., Proceedings of ACM Computer Human Interface (CHI), May 3-7, 1992, pp. 93-98.
“A New Family of Algorithms for Manipulating Compressed Images”, Smith et al., IEEE Computer Graphics and Applications, 1993.
“Anatomy of a Color Histogram”, Novak et al., Proceeding of Computer Vision and Pattern Recognition, Champaign, Ill., June 1992, pp. 599-605.
“Automatic Structure Visualization for Video Editing”, Ueda et al., InterCHI'93 Conference Proceedings, Amsterdam, The Netherlands, 24-29 Apr. 1993, pp. 137-141.
“Automatic Video Indexing and Full-Video Search for Object Appearances”, Nagasaka et al., Proceedings of the IFIP TC2/WG2.6 Second Working Conference on Visual Database Systems, North Holland, Sep. 30-Oct. 3, 1991, pp. 113-127.
“Color Indexing”, Swain et al., International Journal of Computer Vision, vol. 7, No. 1, 1991, pp. 11-32.
“Content Oriented Visual Interface Using Video Icons for Visual Database Systems”, Tonomura et al., Journal of Visual Languages and Computing (1990) 1, pp. 183-198.
“Developing Power Tools for Video Indexing and Retrieval”, Zhang et al., Proceedings of SPIE Conference on Storage and Retrieval for Image and Video Databases, San Jose, Calif., 1994.
“Image Information Systems: Where Do We Go From Here?”, Chang et al., IEEE Transactions on Knowledge and Data Engineering, vol. 4, No. 5, October 1992, pp. 431-442.
“Image Processing on Compressed Data for Large Video Databases”, Arman et al., Proceedings of First ACM International Conference on Multimedia, Anaheim, Calif., 1-6 Aug. 1993, pp. 267-272.
“Image Processing on Encoded Video Sequences”, Arman et al., ACM Multimedia Systems Journal, to appear 1994.
“Impact: An Interactive Natural-Motion-Picture Dedicated Multimedia Authoring System”, Ueda et al., Proceedings of Human Factors in Computing Systems (CHI91), New Orleans, La., Apr. 27-May 2, 1991, pp. 343-350.
“MPEG: A Video Compression Standard for Multimedia Applications”, Le Gall, Communications of the ACM, vol. 34, No. 4, April 1991, pp. 47-58.
“News On-Demand for Multimedia Networks”, Miller et al., ACM International Conference on Multimedia, Anaheim, Calif., 1-6, August 1993, pp. 383-392.
“Overview of the px64 kbit/s Video Coding Standard”, Liou, Communications of the ACM, vol. 34, No. 4, April 1991, pp. 60-63.
“Pattern Recognition by Moment Invariants”, Hu et al., Proc. IRE, vol. 49, 1961, p. 1428.
“Pattern Recognition Experiments in the Mandala/Cosine Domain”, Hsu et al., IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-5, No. 5, September 1983, pp. 512-520.
“The JPEG Still Picture Compression Standard”, Wallace, Communications of the ACM, vol. 34, No. 4, April 1991, pp. 31-44.
“The Revised Fundamental Theorem of Moment Invariants”, Reiss, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, No. 8, August 1991, pp. 830-834.
“VideoMAP and VideoSpaceIcon: Tools for Anatomizing Video Content”, Tonomura et al., Inter CHI'93 Conference Proceedings, Amsterdam, The Netherlands, 24-29 April, 1993, pp. 131-136.
“Visual Pattern Recognition by Moment Invariants”, IRE Trans. Inform. Theory, vol. 8, February 1962, pp. 179-187.
“Watch—Grab—Arrange—See: Thinking with Motion Images via Streams and Collages”, Elliott, Ph.D. Thesis, MIT, February 1993.
Book entitled Digital Image Processing, by Gonzalez et al., Addison-Wesley, Readings, Mass., 1977.
Book entitled Digital Picture Processing by Rosenfeld et al., Academic Press, Orlando, Fla., 1982.
Book entitled Discrete Cosine Transform—Algorithms, Advantages, Applications, by Rao et al., Academic Press, Inc., 1990.
Book entitled Sequential Methods in Pattern Recognition and Machine Learning, Fu, Academic, NY, N.Y. 1968.
C.-C. J. Kuo (ed), “Multimedia Storage and Archiving Systems”, SPIE Proc. Vol. 2916 (11/18-11/22/96).
U.S. Pat. No. 5,600,775, expressly incorporated herein by reference, relates to a method and apparatus for annotating full motion video and other indexed data structures. U.S. Pat. No. 5,428,774, expressly incorporated herein by reference relates to a system of updating an index file of frame sequences so that it indexes non-is overlapping motion image frame sequences. U.S. Pat. No. 5,550,965, expressly incorporated herein by reference, relates to a method and system for operating a data processor to index primary data in real time with iconic table of contents. U.S. Pat. No. 5,083,860, expressly incorporated herein by reference, relates to a method for detecting change points in motion picture images. U.S. Pat. No. 5,179,449, expressly incorporated herein by reference, relates to a scene boundary detecting apparatus. See also:
“A show and tell of the QBIC technology—Query By Image Content (QBIC)”, IBM QBIC Almaden web site, pp. 1-4.
“Chaos & Non-Linear Models in Economics”.
“Chaos Theory in the Financial Markets. Applying Fractals, Fuzzy Logic, Genetic Algorithms”.
“Evolutionary Economics & Chaos Theory”.
“Four Eyes”, MIT Media Lab web site; pp. 1-2.
“Frequently asked questions about visual information retrieval”, Virage Incorporated web site; pp. 1-3.
“IBM Ultimedia Manager 1.1 and Clinet Search”, IBM software web site, pp. 1-4.
“Image Detection and Registration”, Digital Image Processing, Pratt, Wiley, New York, 1991.
“Jacob Methodology” @ WWCSAI.diepa.unipa.it/research/projects/jacob/jacob-method.html.
“Market Analysis. Applying Chaos Theory to Invstment & Economics”.
“Photobook”, MIT Media Lab web site; Aug. 7, 1996; pp. 1-2.
“Profiting from Chaos. Using Chaos Theory for Market Timing, Stock Selection & Option”.
“Shape Analysis”, Digital Image Processing, Pratt, Wiley, New York, 1991.
“The QBIC Project”, IBM QBIC Almaden web site, home page (pp. 1-2).
“Virage—Visual Information Retrieval”, Virage Incorporated, home page.
“Virage Products”, Virage Incorporated web site; pp. 1-2.
“Visual Information Retrieval: A Virage Perspective Revision 3”, Virage Incorporated web site; 1995; pp. 1-13.
“Workshop Report: NSF—ARPA Workshop on Visual Information Management Systems”, Virage Incorporated web. site; pp. 1-15.
A. D. Bimbo, et al, “3-D Visual Query Language for Image Databases”, Journal Of Visual Languages & Computing, 1992, pp. 257-271.
A. E. Cawkell, “Current Activities in Image Processing Part III: Indexing Image Collections”, CRITique, vol. 4, No. 8, May 1992, pp. 1-11, ALSIB, London.
A. Pizano et al, “Communicating with Pictorial Databases”, Human-Machine Interactive Systems, pp. 61-87, Computer Science Dept, UCLA, 1991.
A. Yamamoto et al, “Extraction of Object Features from Image and its Application to Image Retrieval”, IEEE 9th International Conference On Pattern Recognition, 1988, 988-991.
A. Yamamoto et al, “Image Retrieval System Based on Object Features”, IEEE Publication No. CH2518-9/87/0000-0132, 1987, pp. 132-134.
A. Yamamoto et al., “Extraction of Object Features and Its Application to Image Retrieval”, Trans. of IEICE, vol. E72, No. 6, 771-781 (Jun. 1989).
A. Yamamoto et al., “Extraction of Object Features from Image and Its Application to Image Retrieval”, Proc. 9th Annual Conference on Pattern Recognition, vol. II, pp. 988-991 (November 1988).
A. Soffer and H. Samet. Retrieval by content in symbolic-image databases. In Symposium on Electronic Imaging: Science and Technology—Storage & Retrieval for Image and Video Databases IV, pages 144-155. IS&T/SPIE, 1996.
Abadi, M., et al, “Authentication and Delegation with Smart-cards”, Oct. 22, 1990, revised Jul. 30, 1992 Report 67, Systems Research Center, Digital Equipment Corp., Palo Alto, Calif.
Advertisement for“TV Decision,” Cablevision, Aug. 4, 1986.
American National Standard, “Financial Institution Retail Message Authentication”, ANSI X9.191986.
American National Standard, “Interchange Message Specification for Debit and Credit Card Message Exchange Among Financial Institutions”, ANSI X9.2-1988.
Antonofs, M., “Stay Tuned for Smart TV,” Popular Science, November 1990, pp. 62-65.
Arman et al., “Feature Management for Large Video Databases”, 1993. (Abstract Only).
ASIAN TECHNOLOGY INFORMATION PROGRAM (ATIP) REPORT: ATIP95.65: Human Computer Interface International, 7/95 Yokohama.
Barber et al. “Ultimedia Manager: Query by Image Content and it's Applications” IEE, 1994, pp. 424-429, January 1994.
Barros, et al. “Indexing Multispectral Images for Content-Based Retrieval”, Proc. 23rd AIPR Workshop on Image and Information Retrieval, Proc. 23rd Workshop, Washington, D.C., October 1994, pp. 25-36.
Belkin, N. J., Croft, W. B., “Information Filtering And Information Retrieval: Two Sides of the Same Coin?”, Communications of the ACM, December 1992, vol. 35, No. 12, pp. 29-38.
Benoit Mandelbrot: “Fractal Geometry of Nature”, W H Freeman and Co., New York, 1983 (orig ed 1977).
Benoit Mandelbrot: “Fractals—Form, Chance and Dimensions”, W H Freeman and Co., San Francisco, 1977.
Bimbo et al., “Sequence Retrieval by Contents through Spatio Temporal Indexing”, IEEE on CD-ROM, pp. 88-92, Aug. 24, 1993.
Bolot, J.; Turletti, T. & Wakeman, I.; “Scalable Feedback Control for Multicast Video Distribution In the Internet”, Computer Communication Review, vol. 24, No. 4 Oct. 1994, Proceedings of SIGCOMM 94, pp. 58-67.
Bos et al., “SmartCash: a Practical Electronic Payment System”, pp. 1-8; August 1990.
Branko Pecar: “Business Forecasting for Management”, McGraw-Hill Book Co., London, 1994.
Brian H Kaye: “A Random Walk Through Fractal Dimensions”, VCH Verlagsgesellschaft, Weinheim, 1989.
Brugliera, Vito, “Digital On-Screen Display—A New Technology for the Consumer Interface”, Symposium Record Cable Sessions. Jun. 11, 1993, pp. 571-586.
Burk et al, “Value Exchange Systems Enabling Security and Unobservability”, Computers & Security, 91990, pp. 715-721.
C. Chang et al, “Retrieval of Similar Pictures on Pictorial Databases”, Pattern Recognition, vol. 24, No. 7, 1991, pp. 675-680.
C. Chang, “Retrieving the Most Similar Symbolic Pictures from Pictorial Databases”, Information Processing & Management, vol. 28, No. 5, 1992.
C. Faloutsos et al, “Efficient and Effective Querying by Image Content”, Journal of Intelligent Information Systems:Integrating Artificial Intelligence and Database Technologies, vol. 3-4, No. 3, July 1994, pp. 231-262.
C. Goble et al, “The Manchester Multimedia Information System”, Proceedings of IEEE Conference, Eurographics Workshop, April, 1991, pp. 244-268.
C. C. Chang and S. Y. Lee. Retrieval of similar pictures on pictorial databases. Pattern Recog., 24(7), 1991.
Case Study: The CIRRUS Banking Network, Comm. ACM 8, 28 pp. 7970-8078, August 1985.
Chalmers, M., Chitson, P., “Bead: Explorations In Information Visualization”, 15th Ann. Intl SIGIR 92/Denmark—June 1992, pp. 330-337.
Chang et al., “Intelligent Database Retrieval by Visual Reasoning”, PROC Fourteenth Annual International Computer Software and Application Conference, 31 Oct.-1 Nov. 1990, pp. 459-464.
Chang, Yuh-Lin, Zeng, Wenjun, Kamel, Ibrahim, Alonso, Rafael, “Integrated Image and Speech Analysis for Content-Based Video Indexing”.
Chaum et al, “Untraceable Electronic Cash”, Advances in Cryptology, 1988, pp. 319-327.
Chaum et al; “Achieving Electronic Privacy”, Scientific American, pp. 319-327; 1988.
Chaum, D. “Security without Identification: Card Computers to Make Big Brother Obsolete”, Communications of the ACM, 28(10), October 1985, pp. 1030-1044.
Chaum, D. “Untraceable Electronic Mail, Return Addresses, and Digital Pseudonyms”, Communications of the ACM, vol. 24, No. 2, February, 1981.
Chaum, D., “Achieving Electronic Privacy”, Scientific American, August 1992, pp. 96-101.
Chaum, D. L. et al.; “Implementing Capability-Based Protection Using Encryption”; Electronics Research Laboratory, College of Engineering, University of California, Berkeley, Calif.; Jul. 17, 1978.
Cliff Pickover, Spiral Symmetry (World Scientific).
Cliff Pickover, Chaos in Wonderland: Visual Adventures in a Fractal World (St. Martin's Press).
Cliff Pickover, Computers and the Imagination (St. Martin's Press)
Cliff Pickover, Mazes for the Mind: Computers and the Unexpected (St. Martin's Press).
Cliff Pickover, Computers, Pattern, Chaos, and Beauty (St. Martin's Press).
Cliff Pickover, Frontiers of Scientific Visualization (Wiley).
Cliff Pickover, Visions of the Future: Art, Technology, and Computing in the 21st Century (St. Martin's Press).
Cohen, Danny; “Computerized Commerce”; ISI Reprint Series ISI/RS-89/243; October, 1989; Reprinted from Information Processing 89, Proceedings of the IFIP World Computer Congress, held Aug. 28-Sep. 1, 1989.
Cohen, Danny; “Electronic Commerce”; University of Southern California, Information Sciences Institute, Research Report ISI/RR-89-244; October, 1989.
Common European Newsletter, Multimedia Content manipulation and Management, ww.esat.kuleuven.ac.be/˜konijin/ . . .
CompuServe Information Service Users Guide, CompuServe International, 1986, pp. 109-114.
Computer Shopper, Nov. 1994, “Internet for Profit”, pp. 180-182, 187, 190-192, 522-528, 532, 534.
Computer, Vol. 28(9), September 1995.
Compuvid Sales Manual (date unknown).
Corporate Overview, Virage Incorporated web site; pp. 1-4.
Cox, Ingemar J., et al., “PicHunter: Bayesian Relevance Feedback for Image Retrieval,” Proc. of the ICPR '96, IEEE, pp. 361-369.
Cutting, D. R.; Karger, D. R.; Pedersen, J. O. & Tukey, J. W. “Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections”, 15 Ann. Intl SIGIR '92, ACM, 1992, pp. 318-329.
D K Arrowsmith & C M Place: “An Introduction to Dynamical Systems”, Cambridge University Press, Cambridge, 1990.
Damashek, M., Gauging Similarity via N-Grams: Language-Independent Sorting, Categorization, and Retrieval of Text, pp. 1-11, Jan. 24, 1995.
Data Partner 1.0 Simplifies DB Query Routines, PC Week, Sep. 14, 1992, pp. 55 & 58.
David E Rumelhart & James L McClelland: “Parallel Distributed Processing”, Vol 1., The MIT Press, Cambridge, Mass., 1986.
Deering, S.; Estrin, D.; Farinacci, D.; Jacobson, V.; Liu, C.; Wei, L; “An Architecture for Wide-Area Multicast Routing”, Computer Communication Review, vol. 24, No. 4, October 1994, Proceedings of SIGCOMM 94, pp. 126-135.
Donal Daly: “Expert Systems Introduced”, Chartwell-Bratt, Lund, 1988.
Dukach, Seymon; Prototype Implementation of the SNPP Protocol; allspic.lcs.mit.edu; 1992.
E. Binaghi et al, “Indexing and Fuzzy Logic Based Retrieval of Color Images”, Visual Database Systems, II, 1992, pp. 79-92.
E. Binaghi et al., “A Knowledge-Based Environment for Assessment of Color Similarity”, Proc. 2nd Annual Conference on Topics for A1, pp. 268-285 (1990).
E. Lee, “Similarity Retrieval Techniques”, Pictorial Information Systems, Springer Verlag, 1980 pp. 128-176.
E. G. M. Petrakis and C. Faloutsos. Similarity searching in large image databases. Technical Report 3388, Department of Computer Science, University of Maryland, 1995.
Edward Reitman: “Exploring the Geometry of Nature”, Windcrest Books, Blue Ridge Summit, 1989.
Even et al; “Electronic Wallet”; pp. 383-386;1983.
F. J. Varela and P. Bourgine (eds.): Proceedings of the first European Conference on Artificial Life. Cambridge, Mass.: MIT Press. (1991).
Fassihi, Theresa & Bishop, Nancy, “Cable Guide Courting National Advertisers,” Adweek, Aug. 8, 1988.
Flickner, et al. “Query by Image and Video Content, the QBIC System”, IEEE Computer 28(9); 23-32, 1995.
Foltz, P. W., Dumais, S. T., “Personalized Information Delivery: An Analysis Of Information Filtering Methods”, Communications of the ACM, December 1992, vol. 35, No. 12, pp. 51-60.
Frank Pettit: “Fourier Transforms in Action”, Chartwell-Bratt, Lund, 1985.
G F Page, J B Gomm & D Williams: “Application of Neural Networks to Modelling and Control”, Chapman & Hall, London, 1993.
G. Mannes, “Smart Screens”, Video Magazine, December 1993) (2 Pages).
G. Tortora et al, “Pyramidal Algorithms”, Computer Vision, Graphics and Images Processing, 1990, pp. 26-56.
Gautama, S., D'Haeyer, J. P. F., “Context Driven Matching in Structural Pattern Recognition”.
Gautama, S., Haeyer, J. D., “Learning Relational Models of Shape: A Study of the Hypergraph Formalism”.
Gene F Franklin, J David Powell & Abbas Emami-Naeini: “Feedback Control of Dynamic Systems”, Addison-Wesley Publishing Co. Reading, 1994.
George E P Box & Gwilym M Jenkins: “Time Series Analysis: Forecasting and Control”, Holden Day, San Francisco, 1976.
Gifford, D., “Notes on Community Information Systems”, MIT LCS™-419, December 1989.
Gifford, David K.; “Cryptographic Sealing for Information Secrecy and Authentication”; Stanford University and Xerox Palo Alto Research Center; Communication of the ACM; vol. 25, No. 4; April, 1982.
Gifford, David K.; “Digital Active Advertising”; U.S. patent application Ser. No. 08/168,519; filed Dec. 16, 1993.
Gligor, Virgil D. et al.; “Object Migration and Authentication”; IEEE Transactions on Software Engineering; vol. SE-5, No. 6; November, 1979.
Gong et al. “An Image Database System with Content Capturing and Fast Image Indexing Abilities” IEEE, 1994, pp. 121-130, May 1994.
Gregory L Baker & Jerry P Gollub: “Chaotic Dynamics: An Introduction”, Cambridge University Press, Cambridge, 1990.
Gupta, Amarnath; Weymount, Terry & Jain, Ramesh, “Semantic Queries With Pictures: The VI MSYS Model”, Proceedings of the 17th International Conference on Very Large Data Bases, pp. 69-79, Barcelona, September, 1991.
H. Tamura et al, “Image Database Systems: A Survey”, Pattern Recognition, vol. 17, No. 1, 1984, pp. 29-34.
H. Tamura, et al., “Textural Features Corresponding to Visual Perception,” IEEE Transactions on System, Man, and Cyb., vol. SMC-8, No. 6, pp. 460-473 (1978).
H. Samet. The quadtree and related hierarchical data structures. ACM Computing Surveys, 16(2): 187-260, 1984.
Hans Lauwerier: “Fractals—Images of Chaos”, Penguin Books, London, 1991.
Harty et al., “Case Study: The VISA Transaction Processing System,” 1988.
Heinz-Otto Peitgen & Deitmar Saupe: “The Science of Fractal Images”, Springer-Verlag, New York, 1988.
Heinz-Otto Peitgen, Hartmut Jurgens & Deitmar Saupe: “Fractals for the Classroom”, Springer-Verlag, 1992.
Hirata, et al. “Query by Visual Example, Content Based Image Retrieval” Advance in Database Technology—EDBT '92, Springer-Verlag, Berlin 1992, pp. 56-71
Hirzalla et al., “A Multimedia Query User Interface”, IEEE on CD-ROM, pp. 590-593, Sep. 5, 1995.
Hooge, Charles, “Fuzzy logic Extends Pattern Recognition Beyond Neural Networks”, Vision Systems Design, January 1998, pp. 32-37.
Hou et al., “Medical Image Retrieval by Spatial Features”, IEEE on CD-ROM, pp. 1364-1369, Oct. 18, 1992.
lino et al., “An Object-Oriented Model for Spatio-Temporal Synchronization of Multimedia Information”, May, 1994.
Information Network Institute, Carnegie Mellon University, Internet Billing Server, Prototype Scope Document, Oct. 14, 1993.
Ingemar J. Cox et al., “Target Testing and the Pic Hunter Bayesian Multimedia Retrieval System,” Proc. of the 3d Forum on Research and Technology Advances in Digital Libraries, ADL '96, IEEE, pp. 66-75.
Intel Corporation, iPower Technology, Marketing Brochure, date unknown.
Intuit Corp. Quicken User's Guide, “Paying Bills Electronically”, pp. 171-192; undated.
ISO/IEC JTC1/SC29/WG11 N1735, MPEG97, July 1997—Stockholm, “MPEG-7 Applications Document”.
ISO/IEC JTC1/SC29/WG11 N2460, MPEG98, October 1998 “MPEG-7 Context and Objectives (v.10—Atlantic City)”; ISO/IEC JTC1/SC29/WG11 N1920, MPEG97, October 1997 “MPEG-7 Context and Objectives (v.5—Fribourg)”; ISO/IECJTC1/SC29/WG11 N1733, MPEG97, July 1997, “MPEG-7 Context and Objectives (v.4—Stockholm)”.
ISO/IEC JTC1/SC29/WG11 N2461, MPEG98, October 1998—Atlantic City, “MPEG-7 Requirements”.
ISO/IEC JTC1/SC29/WG11 N2462, MPEG98, October 1998—Atlantic City, “MPEG-7 Applications”.
ISO/IEC JTC1/SC29/WG11 N2467, MPEG98, October 1998—Atlantic City, “MPEG-7 Content Set”.
Itzhak Wilt “Computer, Retrieve For Me the Video Clip of the Winning Goal”, Advanced Imaging, August 1998, pp. 53-55.
Ivar Ekeland: “Mathematics and the Unexpected”, The University of Chicago Press, Chicago, 1988
Kenneth Falconer: “Fractal Geometry”, John Wiley & Sons, Chichester, 1990.
Ivars Peterson: “The Mathematical Tourist”, W H Freeman, New York, 1988.
Iyengar et al., “Codes Designs for Image Browsing”, 1994.
J W Bruce & P J Giblin: “Curves and Singularities”, Cambridge University Press, Cambridge, 1992.
J. Hasegawa et al, “Intelligent Retrieval of Chest X-Ray Image Database Using Sketches”, System And Computers In Japan, 1989, pp. 29-42.
J. M. Chassery, et al., “An Interactive Segmentation Method Based on Contextual Color and Shape Criterion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-6, No. 6, (November 1984).
J. Wachman, “A Video Browser that Learns by Example”, Masters Thesis, Massachusetts Institute of Technology; 1996; also appears as MIT Media Laboratory Technical Report No. 383.
J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack. Efficient color histogram indexing for quadratic form distance functions. IEEE Trans. Pattern Anal. Machine Intell., July 1995.
J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. C. Jain, and C. Shu. Virage image search engine: an open framework for image management. In Symposium on Electronic Imaging: Science and Technology—Storage & Retrieval for Image and Video Databases IV, pages 76-87. IS&T/SPIE, 1996.
J. R. Smith and S.-F. Chang. Querying by color regions using the VisualSEEk content-based visual query system. In M. T. Maybury, editor, Intelligent Multimedia Information Retrieval. IJCAI, 1996.
J. R. Smith and S.-F. Chang. Tools and techniques for color image retrieval. In Symposium on Electronic Imaging: Science and Technology—Storage & Retrieval for Image and Video Databases IV, volume 2670, San Jose, Calif., February 1996. IS&T/SPIE.
Jacobs, Charles E., Finkelstein, Adam, Salesin, David H., “Fast Multiresolution Image Querying”.
James Gleick: “Chaos—Making a New Science”, Heinemann, London, 1988.
Jane Hunter, “The Application of Metadata Standards to Video Indexing” www.dtsc.edu.au/RDU/staff/jane-hunter/EuroDL/final.html (<Dec. 24, 1998).
Jim Binkley & Leslie Young, Rama: An Architecture for Internet Information Filtering, Journal of Intelligent Information Systems Integrating Artificial Intelligence and Database Technologies, vol. 5, No. 2, September 1995, pp. 81-99.
Jonathan Berry, “A Potent New Tool for Selling Database Marketing”, Business Week, Sep. 5, 1994, pp. 34-40.
Joseph L McCauley: “Chaos, Dymanics, and Fractals”, Cambridge University Press, Cambridge, 1993.
JPL New Technology Report NPO-20213, Nasa Tech Brief Vol. 22, No. 4, Item #156 (April 1998).
Judith H. Irven et al., “Multi-Media Information Services: A Laboratory Study”, IEEE Communications Magazine, vol. 26, No. 6, June, 1988, pp. 24-44.
K V Mardia, J T Kent & J M Bibby: “Multivariate Analysis”, Academic Press, London, 1979.
K. Hirata et al, “Query by Visual Example Content Based Image Retrieval”, Advances In Database Technology, March, 1992, pp. 57-71.
K. Wakimoto et al, “An Intelligent User Interface to an Image Database using a Figure interpretation Method”, IEEE Publication No. CH2898-5/90/0000/0516, 1990, pp. 516-520.
K. Woolsey, “Multimedia Scouting”, IEEE Computer Graphics And Applications, July 1991 pp. 26-38.
Kelly et al. “Efficiency Issues Related to Probability Density Function Comparison”, SPIE vol. 2670, pp. 42-49 Jan. 1996.
Kelly, P. M., et al. “Candid Comparison Algorithm for Navigating Digital Image Databases”, Proceedings 7th International Working Conference on Scientific and Statistical Database Management, pp. 252-258,1994.
Krajewski, M. et al, “Applicability of Smart Cards to Network User Authentication”, Computing Systems, vol. 7, No. 1, 1994.
Krajewski, M., “Concept for a Smart Card Kerberos”, 15th National Computer Security Conference, October 1992.
Krajewski, M., “Smart Card Augmentation of Kerberos, Privacy and Security Research Group Workshop on Network and Distributed System Security”, February 1993.
Lampson, Butler; Abadi, Martin; Burrows, Michael; and Wobber, Edward; “Authentication in Distributed Systems: Theory and Practice”; ACM Transactions on Computer Systems; vol. 10, No. 4; November, 1992; pp. 265-310.
Landis, Sean, “Content-Based Image Retrieval Systems for Interior Design”, www.tc.cornell.edu/Visualization/Education/cs718/fall1995/landis/index.html.
Langton C G (ed): Artificial Life; Proceedings of the first international conference on Artificial life, Redwood City: Addison-Wessley (1989).
Lee et al., “Video Indexing—An Approach based on Moving Object and Track”, Proceedings of Storage and Retrieval for Image and Video Databases, pp. 25-36. February 1993.
Lee, Denis, et al., “Query by Image Content Using Multiple Objects and Multiple Features: User Interface Issues,” 1994 Int'l Conf. on Image Processing, IEEE, pp. 76-80.
Lennart Ljung & Torsten Soderstrom: “Theory and Practice of Recursive Identification”, The MIT Press, Cambridge, Mass., 1983.
Lennart Ljung: “System Identification; Theory for the User”, Prentice-Hall Englewood Cliffs, N.J., 1987.
Loeb, S., “Architecting Personalized Delivery of Multimedia Information”, Communications of the ACM, December 1992, vol. 35, No. 12, pp. 39-50.
M V Berry, I C Persival & N O Weiss: “Dynamical Chaos”, The Royal Society, London, 1987, Proceedings of a Royal Society Discussion Meeting held on 4 & 5 Feb. 1987.
M. Bender, “EFTS: Electronic Funds Transfer Systems”, Kennikat Press, Port Washington, N.Y., pp. 43-461975.
M. H. O'Docherty et al, “Multimedia Information System—The Management and Semantic Retrieval of all Electronic Data Types”, The Computer Journal, vol. 34, No. 3, 1991.
M. Ioka, “A Method of Defining the Similarity of Images on the Basis of Color Information”, Bulletin Of The National Museum Of Ethnology Special Issue, pp. 229-244, No. 17, November 1992.
M. Kurokawa, “An Approach to Retrieving Images by Using their Pictorial Features”, IBM Research, Japan, Sep. 1989.
M. Swain et al, “Color Indexing”, International Journal Of Computer Vision, 1991, pp. 12-32.
M. Stricker and A. Dimai. Color indexing with weak spatial constraints. In Symposium on Electronic Imaging: Science and Technology—Storage & Retrieval for Image and Video Databases IV, pages 29-41. IS&T/SPIE, 1996.
M. Stricker and M. Orengo. Similarity of color images. In Storage and Retrieval for Image and Video Databases III, volume SPIE Vol. 2420, February 1995.
Mackay et al., “Virtual Video Editing in Interactive Multimedia Applications”, 1989.
Manners, George, “Smart Screens; Development of Personal Navigation Systems for TV Viewers,” Video Magazine, December 1993.
Martin Casdagli & Stephen Eubank: “Nonlinear Modelling and Forecasting”, Addison-Wesley Publishing Co., Redwood City, 1992.
Martinez et al. “Imagenet: A Global Distribution Database for Color Image Storage and Retrieval in Medical Imaging Systems” IEEE, 1992, 710-719, May 1992.
Marvin A. Sirbu; Internet Billing Service Design And Prototype Implementation; pp. 1-19; An Internet Billing Server.
Masahiro Morita & Yoichi Shinoda, Information Filtering Based on User Behavior Analysis and Best Match Text Retrieval, Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, Dublin, Jul. 3-6, 1994, Pages Title Page (272)-281.
Medvinsy et al, “NetCash: A Design for Practical Electronic Currency on the Internet”, Proc. 1st ACM Conf. on Comp. and Comm. Security, November 1993.
Medvinsy et al., “Electronic Currency for the Internet”, Electronic Markets, pp. 30-31, Sep. 1993.
Meyer, J. A., Roitblat, H. L., Wilson, W. (eds.): From Animals to Animats. Proceedings of the Second International Conference on Simulation of Adaptive Behaviour. Cambridge, Mass.: MIT Press. (1991).
Middleton, G. V. ed., 1991, Nonlinear Dynamics, Chaos and Fractals, with Applications to Geological Systems. Geol. Assoc. Canada Short Course Notes Vol. 9 (available from the GAC at Memorial University of Newfoundland, St. John's NF A1 B 3X5).
Mills, “Media Composition for Casual Users”, 1992.
Minneman et al., “Where Were We: making and using near-synchronous, pre-narrative video”, Multimedia '93, pp. 1-11. Dec. 1993.
N. Hutheesing, “Interactivity for the passive”, Forbes magazine Dec. 6, 1993 (@ Forbes Inc. 1993) (2 pages).
N. S. Chang et al., “Query-by-Pictorial Example”, IEEE Transactions on Software Engineering, vol. SE-6, No. 6, pp. 519-524 (November 1980).
N. S. Chang, et al., “Picture Query Languages for Pictorial Data-Base Systems”, Computer vol. 14, No. 11, pp. 23-33 (November 1981).
Nagasaka et al., “Automatic Video Indexing and Full-Video Search for Object Appearances”, Visual Database Systems, (Knuth et al., eds.), pp. 113-126. January 1992.
National Westminster Bank Group Brochure; pp. 1-29; undated.
Needham, Roger M. and Schroeder, Michael D.; “Using Encryption for Authentication in Large Networks of Computers”; Communications of the ACM; vol. 21, No. 12; December, 1978; pp. 993-999.
Needham, Roger M.; “Adding Capability Access to Conventional File Servers”; Xerox Palo Alto Research Center; Palo Alto, Calif.
Newman, B. C., “Proxy-Based Authorization and Accounting for Distributed Systems”, Proc. 13th Int. Conf. on Dist. Comp. Sys., May 1993.
Niblack, W. et al., “The QBIC Project: Querying Images by Content Using Color, Texture, and Shape”, IBM Computer Science Research Report, pp. 1-20 (Feb. 1, 1993).
Nussbaumer et al., “Multimedia Delivery on Demand: Capacity Analysis and Implications”, Proc 19th Conference on Local Computer Networks, 2-5 Oct. 1994, pp. 380-386.
O. Guenther and A. Buchmann. Research issues in spatial databases. In ACM SIGMOD Record, volume 19, December 1990.
Okamoto et al; “Universal Electronic Cash”, pp. 324-337; 1991.
Ono, Atsushi, et al., “A Flexible Content-Based Image Retrieval System with Combined Scene Description Keyword,” Proc. of Multimedia '96, IEEE, pp. 201-208.
Otis Port, “Wonder Chips-How They'll Make Computing Power Ultrafast and Ultracheap”, Business Week, Jul. 4, 1994, pp. 86-92.
P G Drazin: “Nonlinear System”, Cambridge University Press, Cambridge, 1992.
P. Stanchev et al, “An Approach to Image Indexing of Documents”, Visual Database Systems, II, 1992, pp. 63-77.
Peter J Diggle: “Time Series: A Biostatistical Introduction”, Clarendon Press, Oxford, 1990.
Peters:“Chaos and Order in the Capital Markets”, Wiley, 1991
Gershenfeld & Weigend: “The Future of Time Series”, Addison-Wesley, 1993.
Pfitzmann et al; “How to Break and Repair a Provably Secure Untraceable Payment System”; pp. 338-350; 1991
Phillips, “MediaView: a general multimedia digital publication system”, Comm. of the ACM, v. 34, n. 7, pp. 75-83. July 1991.
Predrag Cvitanovic: “Universality in Chaos”, Adam Hilger, Bristol, 1989.
R. Mehrotra et al, “Shape Matching Utilizing Indexed Hypotheses Generation and Testing”, IEEE Transactions On Robotics, vol. 5, No. 1, February 1989, pp. 70-77.
R. Price, et al., “Applying Relevance Feedback to a Photo Archival System”, Journal of Information Science 18, pp. 203-215 (1992).
R. W. Picard et al, “finding Similar Patterns in Large Image Databases”, IEEE ICASSP, Minneapolis, Minn., vol. V, pp. 161-164, April 1993; also appears in MIT Media Laboratory Technical Report No. 205.
Rangan et al., “A Window-based Editor for Digital Video and Audio”, January 1992.
Richards et al., “The Interactive Island”, IEE Revies, July/August 1991 pp. 259-263.
Rivest, R.; “The MD5 Message-Digest Algorithm”; MIT Laboratory for Computer Science and RSA Data Security, Inc.; April, 1992.
Rivest, R. L. et al., “A Method for Obtaining Digital Signatures and Public-Key Cryptosystems,” Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, Mass.
Rivest, R. L.; Shamir, A. & Adleman, L.; “A Method for Obtaining Digital Signatures and Public-Key Cryptosystems”, Communications of the ACM, February 1978, vol. 21, No. 2, pp. 120-126.
Robert Brown: “Statistical Forecasting for Inventory Control”, McGraw-Hill Book Co., New York, 1958.
Robinson, G., and Loveless, W., “Touch-Tone′ Teletext—A Combined Teletext-Viewdata System,” IEEE Transactions on Consumer Electronics, vol. CE-25, No. 3, July 1979, pp. 298-303.
Roizen, Joseph, “Teletext in the USA,” SMPTE Journal, July 1981, pp. 602-610.
Rose, D. E.; Mander, R.; Oren, T., Ponceleon, D. B.; Salomon, G. & Wong, Y. Y. “Content Awareness in a File System Interface Implementing the ‘Pile’ Metaphor for Organizing Information”, 16 Ann. Intl SIGIR '93, ACM, pp. 260-269.
Ross Anderson, “Why Cryptosystems Fail”, Proc. 1st Conf. Computer and Comm. Security, pp. 215-227, November 1993.
Ross J. Anderson, “UEPS—A Second Generation Electronic Wallet”, Proc. of the Second European Symposium on Research in Computer Security (ESORICS), Touluse, France, pp. 411-418, Touluse, France.
Rui, Yong, Huang, Thomas S., Chang, Shih-Fu, “Image Retrieval: Past Present and Future”.
Rui, Yong, Huang, Thomas S., Mehotra, Sharad, “Browsing and retrieving Video Content in a Unified Framework”.
Rui, Yong, Huang, Thomas S., Ortega, Michael, Mehotra, Sharad, “Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval”.
S. Chang et al, “An Intelligent Image Database System”, IEEE Transactions On Software Engineering, vol. 14, No. 5, May 1988, pp. 681-688.
S. Chang et al, “Iconic Indexing by 2-D Strings”, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. PAMI-9, No. 3, May 1987.
S. Chang et al, “Iconic Indexing by 2-D Strings”, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 9, No. 3, May 1987, pp. 413-427.
S. Charles et al, “Using Depictive Queries to Search Pictorial Databases”, Human Computer Interaction, 1990, pp. 493-498.
S. Lee et al, “2D C-string: A New Spatial Knowledge Representation for Image Database Systems”, Pattern Recognition, vol. 23, 1990, pp. 1077-1087.
S. Lee et al, “Similarity Retrieval of Iconic Image Database”, Pattern Recognition, vol. 22, No. 61989, pp. 675-682.
S. Lee et al, “Spatial Reasoning and Similarity Retrieval of Images Using 2D C-string Knowledge Representation”, Pattern Recognition, 1992, pp. 305-318.
S. Negandaripour et al “Challenges in Computer Vision: Future Research Direction”, IEEE Transactions On Systems, Man And Cybernetics, pp. 189-199, 1992, at Conference on Computer Vision and Pattern Recognition.
S. Tanaka et al, “Retrieval Method for an Image Database based on Topological Structure”, SPIE, vol. 1153, 1989, pp. 318-327.
S.-F. Chang. Compressed-domain techniques for image/video indexing and manipulation. In Proceedings, I.E.E.E. International Conference on Image Processing, Washington, D.C., October 1995. invited paper to the special session on Digital Library and Video on Demand.
S.-K. Chang, Q. Y. Shi, and C. Y. Yan. Iconic indexing by 2-D strings. IEEE Trans. Pattern Anal. Machine Intell., 9(3):413-428, May 1987.
S.-K. Chang. Principles of Pictorial Information Systems Design. Prentice Hall, 1989.
Salton, G., “Developments in Automatic Text Retrieval”, Science, vol. 253, pp. 974-980, Aug. 30, 1991.
Schamuller-Bichl, I., “IC-Cards in High-Security Applications”, in Selected Papers from the Smart Card 2000 Conference, Springer Verlag, 1991, pp. 177-199.
Semyon Dukach, “SNPP: A Simple Network Payment Protocol”, MIT Laboratory for Computer Science, Cambridge, Mass., 1993.
Shann et al. “Detection of Circular Arcs for Content-Based Retrieval from an Image Database” IEE Proc.-Vis. Image Signal Process, vol. 141, No. 1, February 1994, pp. 49-55.
Sheldon G Lloyd & Gerald D Anderson: “Industrial Process Control”, Fisher Controls Co., Marshalltown, 1971.
Sheth et al., “Evolving Agents for Personalized Information Filtering”, 1-5 Mar. 1993, pp. 345-352.
Sheth, B. & Maes, P. “Evolving Agents For Personalized Information Filtering”, Proc. 9th IEEE Conference, 1993 pp. 345-352.
Sincoskie, W. D. & Cotton C. J. “Extended Bridge Algorithms for Large Networks”, IEEE Network, January 1988-vol. 2, No. 1, pp. 16-24.
Smith, J. et al., “Quad-Tree Segmentation for Texture-Based Image Query” Proceeding ACM Multimedia 94, pp. 1-15, San Francisco, 1994.
Smoliar, S. et al., “Content-Based Video Indexing and Retrieval”, IEEE Multimedia, pp. 62-72 (Summer 1994).
Society for Worldwide Interbank Financial Telecommunications S.C., “A.S.W.I.F.T. Overview”, undated.
Spyros Makridakis & Steven Wheelwright: “The Handbook of Forecasting”, John Wiley, New York, 1982.
Steven C Chapra & Raymond P Canale: “Numerical Methods for Engineers”, McGraw-Hill Book Co., New York, 1988.
T. Arndt, “A Survey of Recent Research in Image Database Management”, IEEE Publication No. TH0330-1/90/0000/0092, pp. 92-97,1990.
T. Gevers et al, “Enigma: An Image Retrieval System”, IEEE 11th IAPR International Conference On Pattern Recognition, 1992, pp. 697-700.
T. Gevers et al, “Indexing of Images by Pictorial Information”, Visual Database Systems, II, 1992 IFIP, pp. 93-101
T. Kato et al, “A Cognitive Approach Interaction”, International Conference Of Multimedia Information Systems, January, 1991, pp. 109-119.
T. Kato et al, “Trademark: Multimedia Database with Abstracted Representation on Knowledge Base”, Proceedings Of The Second International Symposium On Interoperable Information Systems, pp. 245-252, Nov. 1988.
T. Kato et al, “Trademark: Multimedia Image Database System with Intelligent Human Interface”, System And Computers In Japan, 1990, pp. 33-46.
T. Kato, “A Sketch Retrieval Method for Full Color Image Database-Query by Visual Example”, IEEE, Publication No. 0-8186-2910-X/92,1992, pp. 530-533.
T. Kato, “Intelligent Visual Interaction with Image Database Systems Toward the Multimedia Personal Interface”, Journal Of Information Processing, vol. 14, No. 2, 1991, pp. 134-143.
T. Minka, “An Image Database Browser that Learns from User Interaction”, Masters Thesis, Massachusetts Institute of Technology; 1996; also appears as MIT Media Laboratory Technical Report 365.
T.-S. Chua, S.-K. Lim, and H.-K. Pung. Content-based retrieval of segmented images. In Proc. ACM Intern. Conf. Multimedia, October 1994.
Tak W. Yan & Hector Garcia-Molina, SIFT—A Tool for Wide-Area Information Dissemination, 1995 USENIX Technical Conference, New Orleans, La., Jan. 16-20, pp. 177-186.
Tanton, N. E., “UK Teletext—Evolution and Potential,” IEEE Transactions on Consumer Electronics, vol. CE-25, No. 3, July 1979, pp. 246-250.
Tenenbaum, Jay M. and Schiffman, Allan M.; “Development of Network Infrastructure and Services for Rapid Acquisition”; adapted from a white paper submitted to DARPA by MCC in collaboration with EIT and ISI.
Training Computers To Note Images, New York Times, Apr. 15, 1992.
Turcotte, Donald L., 1992, Fractals and Chaos in Geology and Geophysics. Cambridge U.P.
TV Communications Advertisement for MSI Datacasting Systems, January 1973.
V. Gudivada et al, “A Spatial Similarity Measure for Image Database Applications”, Technical Report 91-1, Department of Computer Science, Jackson, Miss., 39217, 1990-1991.
V. N. Gudivada and V. V. Raghavan. Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Trans. on Information Systems, 13(2), April 1995.
Vittal, J., “Active Message Processing: Message as Messengers”, pp. 175-195; 1981.
Voydock, Victor et al.; “Security Mechanisms in High-Level Network Protocols”; Computing Surveys; vol. 15, No. 2; June 1981.
W Gellert, H Kustner, M Hellwich & H Kastner: “The VNR Concise Encyclopedia of Mathematics”, Van Nostrand Reinhols Co., New York, 1975.
W. Grosky et al, “A Pictorial Index Mechanism for Model-based Matching”, Data 7 Knowledge Engineering 8, 1992, pp. 309-327.
W. Grosky et al, “Index-based Object Recognition in Pictorial Data Management”, Computer Vision, 1990, pp. 416-436.
W. Niblack et al, “Find me the Pictures that Look Like This: IBM′S Image Query Project”, Advanced Imaging, April 1993, pp. 32-35.
W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, and C. Faloutsos. The QBIC project: Querying images by content using color, texture, and shape. In Storage and Retrieval for Image and Video Databases, volume SPIE Vol. 1908, February 1993.
W. T. Freeman et al, “The Design and Use of Steerable Filters”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, No. 9, September 1991, pp. 891-906.
Weber et al., “Marquee: A Tool for Real-Time Video Logging”, CHI'94. April 1994.
Willett, P., “Recent Trends in Hierarchic Document Clustering: A Critical Review”, Information Processing & Management, vol. 24, No. 5, pp. 557-597, 1988
William L. Thomas, “Electronic Program Guide Applications—The Basics of System Design”, 1994 NCTA Technical Papers, pp. 15-20.
X. Zhang, et al, “Design of a Relational Image Database Management System: IMDAT”, IEEE Publication No. TH0166-9/87/0000-0310, 1987, pp. 310-314.
Y. Okada, et al., “An Image Storage and Retrieval System for Textile Pattern Adaptable to Color Sensation of the Individual”, Trans. Inst. Elec. Inf. Comm., vol. J70D, No. 12, pp. 2563-2574, December 1987 (Japanese w/English Abstract).
Y. Yoshida et al, “Description of Weather Maps and Its Application to Implementation of Weather Map Database”, IEEE 7th International Conference On Pattern Recognition, 1984, pp. 730-733.
Yan et al., “Index Structures for Information Filtering Under the Vector Space Model”, PROC the 10th International Conference on Data Engineering, pp. 14-18 of DRD203RW User's Manual relating to the DSS Digital System.
Z. Chen et al, “Computer Vision for Robust 3D Aircraft Recognition with Fast Library Search”, Pattern Recognition, vol. 24, No. 5, pp. 375-390, 1991, printed in Great Britain.
Zhuang, Yueting, Rui, Yong, Huang, Thomas S., Mehotra, Sharad, “Applying Semantic Association to Support Content-Based Video Retrieval”.
Video on Demand
Video on demand has long been sought as a means for delivering personalized media content. The practical systems raise numerous issues, including data storage formats, retrieval software, server hardware architecture, multitasking and buffering arrangements, physical communications channel, logical communications channel, receiver and decoder system, user interface, etc. In addition, typically a pay-per-view concept may be employed, with concomitant subscription, royalty collection and accounting issues. See, e.g.:
A. D. Gelman, et al.: A Store-And-Forward Architecture For Video-On-Demand Service; ICC 91 Conf.; June 1991; pp. 842-846.
Caitlin Bestler: Flexible Data Structures and Interface Rituals For Rapid Development of OSD Applications; 93 NCTA Tech. Papers; Jun. 6, 1993; pp. 223-236.
Consumer Digest advertisement: Xpand Your TV's Capability: Fall/Winter 1992; p. 215.
Daniel M. Moloney: Digital Compression in Todays Addressable Enviroment; 1993 NCTA Technical Papers; Jun. 6, 1993; pp. 308-316.
Great Presentations advertisement: Remote, Remote; 1987; p. 32H.
Henrie van den Boom: An Interactive Videotex System for Two-Way CATV Networks; AEU, Band 40; 1986; pp. 397-401.
Hong Kong Enterprise advertisement: Two Innovative New Consumer Products From SVI; November 1988; p. 379.
IEEE Communications Magazine; vol. 32, No. 5, May 1994 New York, N.Y., US, pp. 68-80, XP 000451097 Chang et al “An Open Systems Approach to Video on Demand”.
Proceedings of the IEEE, vol. 82, No. 4, April 1994 New York, N.Y., US, pp. 585-589, XP 000451419 Miller “A Scenario for the Deployment of Interactive Multimedia Cable Television Systems in the United States in the 1990's”.
Reimer, “Memories in my Pocket”, Byte, pp. 251-258, February 1991.
Sharpless, “Subscription teletext for value added services”, August 1985.
Demographically Targeted Advertising Through Electronic Media
Since the advent of commercially subsidized print media, attempts have been made to optimize the placement and compensation aspects relating to commercial messages or advertisements in media. In general, advertisers subsidize a large percentage of the cost of mass publications and communications, in return for the inclusion and possibly strategic placement of advertisements in the publication. Therefore, the cost of advertising in such media includes the cost of preparation of the advertisement, a share of the cost of publication and a profit for the content provider and other services. Since the advertiser must bear some of the cost of production and distribution of the content, in addition to the cost of advertisement placement itself, the cost may be substantial. The advertiser justifies this cost because the wide public reception of the advertisement, typically low cost per consumer “impression”, with a related stimulation of sales due to commercial awareness of the advertisers' products and services. Therefore, the advertisement is deemed particularly effective if either the audience is very large, with ad response proportionate to the size of the audience, or if it targets a particularly receptive audience, with a response rate higher than the general population.
On the other hand, the recipient of the commercial publication is generally receptive of the advertisement, even though it incurs a potential inefficiency in terms of increased data content and inefficiencies in receiving the content segment, for two reasons. First, the advertisements subsidize the publication, lowering the monetary cost to the recipient. Second, it is considered economically efficient for a recipient to review commercial information relating to prospective purchases or expenditures, rather than directly soliciting such information from the commercial source, i.e., “push” is better than “pull”. For this reason specialty publications are produced, including commercial messages appropriate for the particular content of the media or the intended recipients. In fact, in some forms of publications, most, if not all the information content is paid advertisements, with few editorial or independently produced pieces.
Mass media, on the other hand, tends not to include specialty commercial messages, because the interested population is too disperse and the resulting response rate from an advertisement too low, and further because the majority of the audience will be disinterested or even respond negatively to certain messages. Thus, mass media generally includes a majority of retail advertisements, with specialty advertisements relegated, if at all, to a classified section which is not interspersed with other content.
This is the basis for a “least common denominator” theory of marketing, that mass media must merchandise to the masses, while specialty media merchandises to selected subpopulations. As a corollary, using such types of media, it may be difficult to reach certain specialized populations who do not consistently receive a common set of publications or who receive primarily publications which are unspecialized or directed to a different specialty.
Where a recipient has limited time for reviewing media, he or she must divide his or her available time between mass media and specialty media. Alternatively, publication on demand services have arisen which select content based on a user's expressed interests. Presumably, these same content selection algorithms may be applied to commercial messages. However, these services are primarily limited distribution, and have content that is as variable as commercial messages. Likewise, mass media often has regionally variable content, such as local commercials on television or cable systems, or differing editions of print media for different regions. Methods are known for demographic targeting of commercial information to consumers; however, both the delivery methods and demographic targeting methods tend to be suboptimal.
Sometimes, however, the system breaks down, resulting in inefficiencies. These result where the audience or a substantial proportion thereof is inappropriate for the material presented, and thus realize a low response rate for an advertiser or even a negative response for the media due to the existence of particular commercial advertisers. The recipients are bombarded with inappropriate information, while the advertiser fails to realize optimal return on its advertising expenditures. In order to minimize the occurrence of these situations, services are available, including A. C. Nielsen Co. and Arbitron, Inc., which seek to determine the demographics of the audience of broadcast media.
U.S. Pat. No. 5,436,653, incorporated herein by reference, relates to a broadcast segment recognition system in which a signature representing a monitored broadcast segment is compared with broadcast segment signatures in a data base representing known broadcast segments to determine whether a match exists. Therefore, the broadcast viewing habits of a user may be efficiently and automatically monitored, without pre-encoding broadcasts or the like.
U.S. Pat. No. 5,459,306, incorporated herein by reference, relates to a method for delivering targeting information to a prospective individual user. Personal user information is gathered, as well as information on a user's use of a product, correlated and stored. Classes of information potentially relevant to future purchases are then identified, and promotions and recommendations delivered based on the information and the user information.
U.S. Pat. No. 5,483,278, incorporated herein by reference, relates to a system having a user interface which can access downloaded electronic programs and associated information records, and which can automatically correlate the program information with the preferences of the user, to create and display a personalized information database based upon the results of the correlation. Likewise, U.S. Pat. No. 5,223,914, expressly incorporated herein by reference, relates to a system and method for automatically correlating user preferences with a T.V. program information database.
U.S. Pat. No. 5,231,494, expressly incorporated herein by reference, relates to a system which selectively extracts one of a plurality of compressed television signals from a single channel based on viewer characteristics.
U.S. Pat. No. 5,410,344 relates to a system for selecting video programs based on viewers preferences, based on content codes of the programs.
U.S. Pat. No. 5,485,518, incorporated herein by reference, relates to a system for electronic media program recognition and choice, allowing, for example, parental control of the individual programs presented, without requiring a transmitted editorial code.
Videoconferencing Technologies
Videoconferencing systems are well known in the art. A number of international standards have been defined, providing various telecommunication bandwidth and communication link options. For example, H.320, H.323 and H.324 are known transport protocols over ISDN, packet switched networks and public switched telephone networks, respectively. H.324 provides a multimedia information communication and videoconferencing standard for communication over the standard “plain old telephone system” network (“POTS”), in which the video signal is compressed using DCT transforms and motion compensation for transmission over a v.80 synchronous v.34-type modem link. The video image is provided as a video window with relatively slow frame rate. This image, in turn, may be presented on a computer monitor or television system, with appropriate signal conversion. See, Andrew W. Davis, “Hi Grandma!: Is It Time for TV Set POTS Videoconferencing?”, Advanced Imaging, pp. 45-49 (March 1997); Jeff Child, “H.324 Paves Road For Mainstream Video Telephony”, Computer Design, January 1997, pp. 107-110. A newly proposed set of extensions to H.324, called H 0.324/M, provides compatibility with mobile or impaired telecommunications systems, and accommodates errors and distortions in transmissions, reduced or variable transmission rates and other anomalies of known available mobile telecommunications systems, such as Cellular, GSM, and PCS.
Four common standards are employed, which are necessary for videoconferencing stations to communicate with each other under common standards. The first is called h.320, and encompasses relatively high bandwidth systems, in increments of 64 kbits/sec digital communication with a synchronous communication protocol. Generally, these systems communicate with 128 kbits/sec, 256 kbits/sec or 384 kbits/sec, over a number of “bonded” ISDN B-channels. The second standard H.324, employs a standard POTS communication link with a v.80/v.34bis modem, communicating at 33.6 kbits/sec synchronous. The third standard, is the newly established H.321 standard, which provides for videoconferencing over a packet switched network, such as Ethernet, using IPX or TCP/IP. Finally, there are so-called Internet videophone systems, such as Intel Proshare. See, Andrew W. Davis, “The Video Answering Machine: Intel ProShare's Next Step”, Advanced Imaging, pp. 28-30 (March 1997).
In known standards-based videoconferencing systems, the image is generally compressed using a discrete cosine transform, which operates in the spatial frequency domain. In this domain, visually unimportant information, such as low frequencies and high frequency noise are eliminated, leaving visually important information. Further, because much of the information in a videoconference image is repeated in sequential frames, with possible movement, this redundant information is transmitted infrequently and filtered from the transmitted image stream, and described with motion vector information. This motion vector information encodes objects which are fixed or move somewhat between frames. Such known techniques include H.261, with integer pixel motion estimation, and H.263, which provides ½ pixel motion estimation. Other techniques for video compression are known or have been proposed, such as H.263+, and MPEG-4 encoding. Many standard videoconferencing protocols require the initial transmission of a full frame image, in order to set both transmitting and receiving stations to the same encoding state. The digital data describing this image is typically Huffman encoded for transmission. Multiple frames may be combined and coded as a unit, for example as so-called PB frames. Other techniques are also known for reducing image data transmission bandwidth for various applications, including video conferencing.
Each remote videoconference terminal has an interface system, which receives the digital data, and separates the video information (H.261, H.263), audio information (G.711, G.723, G.723.1), data protocol information (HDLC, V.14, LAPM, etc.) and control information (H.245, H.221/H.223) into discrete streams, which are processed separately. Likewise, each terminal interface system also assembles the audio information, video information, data protocols and control data for transmission. The control information consists of various types of information; the standard control protocol which addresses the data format, error correction, exception handling, and other types of control; and the multipoint control information, such as which remote videoconference terminal(s) to receive audio information from, selective audio muting, and such. Generally, the standard, low level control information is processed locally, at the codec interface system, and filtered from the remainder of the multipoint control system, with only the extracted content information made available to the other stations.
The ITU has developed a set of multipoint videoconferencing standards or recommendations, T.120-T.133, T.RES series, H.231, H.243, etc. These define control schemes for multiple party video conferences. Typically, these protocols are implemented in systems which either identically replicate the source image data stream from one source to a plurality of destinations, or completely decode and reencode the image in a different format in a “transcoder” arrangement, to accommodate incompatible conference stations. The ITU standards also allow optional data fields which may be used to communicate digital information essentially outside the videoconference scheme, and provide data conferencing capabilities, which allow videoconferencing and data conferencing to proceed simultaneously. See, ITU T.120-T.127, T.130-T.133, T.RES, T.Share and T.TUD recommendations, expressly incorporated herein by reference.
There are a number of known techniques for transmitting and displaying alphanumeric data on a television, the most common of which are teletext, used primarily in Europe, and closed caption, which is mandated in television sets larger than 13 inches by the Television Decoder Circuitry Act of 1990, and Section 305 of the Telecommunications Act of 1996, and Federal Communication Commission (FCC) regulations. The American closed caption standard is EIA 608. The later is of particular interest because many current generation televisions, especially larger sizes, include a closed caption decoder, and thus require no external hardware or connections, separate from the hardware and cabling for supplying the video signal. See, TCC Tech Facts, Vols. 1-4, (www.wgbh.org, rev. 9/95) expressly incorporated herein by reference. The closed caption signal is distributed on Line 21 of the vertical blanking interval. The existing standard supports 480 bits/sec, with a potential increase to 9600 bits/sec in the forthcoming ATSC standard.
Known systems provide a videoconferencing system which resides in a “set top box”, i.e., a stand-alone hardware device suitable for situation on top of a television set, providing all of the necessary functionality of a videoconferencing system employing the television as the display and possibly audio speaker functions. These systems, however, do not integrate the television functions, nor provide interaction between the video and videoconferencing systems. C-Phone Inc., Wilmington N.C., provides a C-Phone Home product line which provides extensions to H.324 and/or H.320 communications in a set-top box.
Other known videophone and videoconferencing devices are disclosed, e.g., in U.S. Pat. Nos. 5,600,646; 5,565,910; 5,564,001; 5,555,443; 5,553,609; 5,548,322; 5,542,102; 5,537,472; 5,526,405; 5,509,009; 5,500,671; 5,490,208; 5,438,357; 5,404,579; 5,374,952; 5,224,151; 4,543,665; 4,491,694; 4,465,902; 4,456,925; 4,427,847; 4,414,432; 4,377,729; 4,356,509; 4,349,701; 4,338,492; 4,008,376 and 3,984,638 each of which is expressly incorporated herein by reference.
Known Web/TV devices (from Sony/Magnavox/Philips) allow use of a television to display alphanumeric data, as well as audiovisual data, but formats this data for display outside the television. In addition, embedded Web servers are also known. See, Richard A. Quinell, “Web Servers in embedded systems enhance user interaction”, EDN, Apr. 10, 1997, pp. 61-68, incorporated herein by reference. Likewise, combined analog and digital data transmission schemes are also known. See. U.S. Pat. No. 5,404,579.
A class of computing devices, representing a convergence of personal computers and entertainment devices, and which provide network access to the Internet (a publicly available network operating over TCP/IP). ITU standards for communications systems allow the selective addition of data, according to T.120-T.133, T.RES series of protocols, as well as HDLC, V.14, LAPM, to the videoconference stream, especially where excess bandwidth is available for upload or download.
A system may be provided with features enabling it to control a so-called smart house and/or to be a part of a security and/or monitoring system, with imaging capability. These functions are provided as follows. As discussed above, various data streams may be integrated with a videoconference data stream over the same physical link. Therefore, external inputs and outputs may be provided to the videophone or videoconference terminal, which maybe processed locally and/or transmitted over the telecommunications link. The local device, in this case, is provided with a continuous connection or an autodial function, to create a communications link as necessary. Therefore, heating ventilation and air conditioning control (HVAC), lighting, appliances, machinery, valves, security sensors, locks, gates, access points, etc., may all be controlled locally or remotely through interfaces of the local system, which may include logic level signals, relays, serial ports, computer networks, fiber optic interfaces, infrared beams, radio frequency signals, transmissions through power lines, standard-type computer network communications (twisted pair, coaxial cable, fiber optic cable), acoustic transmissions and other known techniques. Likewise, inputs from various devices and sensors, such as light or optical, temperature, humidity, moisture, pressure, fluid level, security devices, radio frequency, acoustic, may be received and processed locally or remotely. A video and audio signal transmission may also be combined with the data signals, allowing enhanced remote monitoring and control possibilities. This information, when transmitted through the telecommunication link, may be directed to another remote terminal, for example a monitoring service or person seeking to monitor his own home, or intercepted and processed at a central control unit or another device. Remote events may be monitored, for example, on a closed caption display mode of a television attached to a videophone.
While the preferred embodiments of the invention adhere to established standards, the present invention also encompasses communications which deviate from or extend beyond such standards, and thus may engage in proprietary communications protocols, between compatible units.
Other References
In addition, the following patents are considered relevant to the data compression and pattern recognition functions of the apparatus and interface of the present invention and are incorporated herein by reference: U.S. Pat. Nos. 3,609,684; 3,849,760; 3,950,733; 3,967,241; 4,025,851; 4,044,243; 4,100,370; 4,118,730; 4,148,061; 4,213,183; 4,225,850; 4,228,421; 4,230,990; 4,245,245; 4,254,474; 4,264,924; 4,264,925; 4,305,131; 4,326,259; 4,331,974; 4,338,626; 4,390,904; 4,395,780; 4,420,769; 4,442,544; 4,449,240; 4,450,531; 4,468,704; 4,491,962; 4,499,601; 4,501,016; 4,511,918; 4,543,660; 4,546,382; 4,547,811; 4,547,899; 4,581,762; 4,593,367; 4,602,279; 4,630,308; 4,646,250; 4,656,665; 4,658,429; 4,658,370; 4,660,166; 4,677,466; 4,697,209; 4,672,683; 4,677,680; 4,682,365; 4,685,145; 4,695,975; 4,710,822; 4,710,964; 4,716,404; 4,719,591; 4,731,863; 4,734,786; 4,736,439; 4,739,398; 4,742,557; 4,747,148; 4,752,890; 4,653,109; 4,760,604; 4,764,971; 4,764,973; 4,771,467; 4,773,024; 4,773,099; 4,774,677; 4,775,935; 4,783,752; 4,783,754; 4,783,829; 4,789,933; 4,790,025; 4,799,270; 4,802,103; 4,803,103; 4,803,736; 4,805,224; 4,805,225; 4,805,255; 4,809,331; 4,809,341; 4,817,171; 4,817,176; 4,821,333; 4,823,194; 4,829,453; 4,831,659; 4,833,637; 4,837,842; 4,843,562; 4,843,631; 4,845,610; 4,864,629; 4,872,024; 4,876,731; 4,881,270; 4,884,217; 4,887,304; 4,888,814; 4,891,762; 4,893,346; 4,897,811; 4,905,162; 4,905,286; 4,905,296; 4,906,099; 4,906,940; 4,908,758; 4,914,708; 4,920,499; 4,926,491; 4,930,160; 4,931,926; 4,932,065; 4,933,872; 4,941,193; 4,944,023; 4,949,187; 4,956,870; 4,958,375; 4,958,375; 4,964,077; 4,965,725; 4,967,273; 4,972,499; 4,979,222; 4,987,604; 4,989,256; 4,989,258; 4,992,940; 4,995,078; 5,012,334; 5,014,219; 5,014,327; 5,018,218; 5,018,219; 5,019,899; 5,020,112; 5,020,113; 5,022,062; 5,027,400; 5,031,224; 5,033,101; 5,034,991; 5,038,379; 5,038,390; 5,040,134; 5,046,121; 5,046,122; 5,046,179; 5,047,867; 5,048,112; 5,050,223; 5,051,840; 5,052,043; 5,052,045; 5,052,046; 5,053,974; 5,054,093; 5,054,095; 5,054,101; 5,054,103; 5,055,658; 5,055,926; 5,056,147; 5,058,179; 5,058,180; 5,058,183; 5,058,186; 5,059,126; 5,060,276; 5,060,277; 5,060,279; 5,060,282; 5,060,285; 5,061,063; 5,063,524; 5,063,525; 5,063,603; 5,063,605; 5,063,608; 5,065,439; 5,065,440; 5,065,447; 5,067,160; 5,067,161; 5,067,162; 5,067,163; 5,067,164; 5,068,664; 5,068,723; 5,068,724; 5,068,744; 5,068,909; 5,068,911; 5,076,662; 5,099,422; 5,103,498; 5,109,431; 5,111,516; 5,119,507; 5,122,886; 5,130,792; 5,132,992; 5,133,021; 5,133,079; 5,134,719; 5,148,497; 5,148,522; 5,155,591; 5,159,474; 5,161,204; 5,168,529; 5,173,949; 5,177,796; 5,179,652; 5,202,828; 5,220,420; 5,220,648; 5,223,924; 5,231,494; 5,239,617; 5,247,347; 5,247,651; 5,259,038; 5,274,714; 5,283,641; 5,303,313; 5,305,197; 5,307,421; 5,315,670; 5,317,647; 5,317,677; 5,343,251; 5,351,078; 5,357,276; 5,381,158; 5,384,867; 5,388,198; 5,390,125; 5,390,281; 5,410,343; 5,410,643; 5,416,856; 5,418,951; 5,420,975; 5,421,008; 5,428,559; 5,428,727; 5,428,730; 5,428,774; 5,430,812; 5,434,933; 5,434,966; 5,436,653; 5,436,834; 5,440,400; 5,446,891; 5,446,919; 5,455,892; 5,459,517; 5,461,699; 5,465,308; 5,469,206; 5,477,447; 5,479,264; 5,481,294; 5,481,712; 5,483,278; 5,485,219; 5,485,518; 5,487,132; 5,488,425; 5,488,484; 5,495,292; 5,496,177; 5,497,314; 5,502,774; 5,504,518; 5,506,768; 5,510,838; 5,511,134; 5,511,153; 5,515,098; 5,515,099; 5,515,173; 5,515,453; 5,515,471; 5,517,598; 5,519,452; 5,521,841; 5,521,984; 5,522,155; 5,523,796; 5,524,065; 5,526,427; 5,535,302; 5,541,638; 5,541,662; 5,541,738; 5,543,929; 5,544,254; 5,546,475; 5,548,667; 5,550,575; 5,550,928; 5,550,965; 5,552,833; 5,553,221; 5,553,277; 5,554,983; 5,555,495; 5,557,728; 5,559,548; 5,560,011; 5,561,649; 5,561,718; 5,561,796; 5,566,274; 5,572,604; 5,574,845; 5,576,950; 5,579,471; 5,581,658; 5,586,218; 5,588,074; 5,592,560; 5,574,845; 5,579,471; 5,581,665; 5,581,800; 5,583,560; 5,586,025; 5,594,661; 5,594,911; 5,596,705; 5,600,733; 5,600,775; 5,604,542; 5,604,820; 5,604,823; 5,606,655; 5,611,020; 5,613,032; 5,614,940; 5,617,483; 5,617,565; 5,621,454; 5,621,484; 5,621,579; 5,621,903; 5,625,715; 5,625,783; 5,627,915; 5,634,849; 5,635,986; 5,642,434; 5,644,686; 5,644,735; 5,654,771; 5,655,117; 5,657,397; 5,659,653; 5,659,368; 5,659,732; 5,664,046; 5,668,897; 5,671,343; 5,671,411; 5,682,437; 5,696,964; 5,701,369; 5,710,601; 5,710,833; 5,710,834; 5,715,400; 5,717,814; 5,724,424; 5,724,472; 5,729,741; 5,734,893; 5,737,444; 5,740,274; 5,745,126; 5,745,640; 5,745,710; 5,751,286; 5,751,831; 5,754,938; 5,758,257; 5,761,655; 5,764,809; 5,767,893; 5,767,922; 5,768,421; 5,768,426; 5,768,437; 5,778,181; 5,797,001; 5,798,785; 5,799,109; 5,801,750; 5,801,753; 5,805,763; 5,809,471; 5,819,288; 5,828,809; 5,835,087; 5,850,352; 5,852,823; 5,857,181; 5,862,260; H 331; and Re. 33,316. The aforementioned patents, some of which are mentioned elsewhere in this disclosure, and which form a part of this disclosure, may be applied in known manner by those skilled in the art in order to practice various embodiments of the present invention.
The following scientific articles, some of which are discussed elsewhere herein, are understood by those skilled in the art and relate to the pattern recognition and image compression functions of the apparatus and interface of the present invention:
“Fractal Geometry-Understanding Chaos”, Georgia Tech Alumni Magazine, p. 16 (Spring 1986).
“Fractal Modelling of Biological Structures”, School of Mathematics, Georgia Institute of Technology (date unknown).
“Fractal Modelling of Real World Images”, Lecture Notes for Fractals: Introduction, Basics and Perspectives, Siggraph (1987).
“Fractals Yield High Compression”, Electronic Engineering Times, Sep. 30, 1991, p. 39.
“Fractals—A Geometry of Nature”, Georgia Institute of Technology Research Horizons, p. 9 (Spring 1986).
“Voice Recognition and Speech Processing”, Elektor Electronics, September 1985, pp. 56-57.
Aleksander, I., “Guide to Pattern Recognition Using Random-Access Memories”, Computers and Digital Techniques, 2(1):29-40 (February 1979).
Anderson, F., W. Christiansen, B. Kortegaard, “Real Time, Video Image Centroid Tracker”, Apr. 16-20, 1990.
Anson, L., M. Barnsley, “Graphics Compression Technology”, SunWorld, pp. 43-52 (October 1991).
Appriou, A., “Interet des theories de l'incertain en fusion de donnees”, Colloque International sur le Radar Paris, 24-28 avril 1989.
Appriou, A., “Procedure d'aide ala decision multi-informateurs. Applications ala classification multi-capteurs de cibles”, Symposium de l'Avionics Panel (AGARD) Turquie, 25-29 avril 1988.
Arrow, K. J., “Social choice and individual valves”, John Wiley and Sons Inc. (1963).
Barnsley et al., “A Better Way to Compress Images”, Byte Magazine, January 1988.
Barnsley et al., “Harnessing Chaos For Images Systhesis”, Computer Graphics, 22(4) (8/1988).
Barnsley et al., “Hidden Variable Fractal Interpolation Functions”, School of Mathematics, Georgia Institute of Technology, Atlanta, Ga. 30332, July, 1986.
Batchelor, B. G., “Pattern Recognition, Ideas in Practice”, Plenum Press, London and New York, (1978).
Batchelor, B. G., “Practical Approach to Pattern Classification”, Plenum Press, London and New York, (1974).
Bellman, R. E., L. A. Zadeh, “Decision making in a fuzzy environment”, Management Science, 17(4) (December 1970).
Bhatnagar, R. K., L. N. Kamal, “Handling uncertain information: a review of numeric and non-numeric methods”, Uncertainty in Artificial Intelligence, L. N. Kamal and J. F. Lemmer, Eds. (1986).
Blair, D., R. Pollack, “La logique du choix collectif”, Pour la Science (1983).
Burr, D. J., “A Neural Network Digit Recognizer”, Proceedings of the 1986 IEEE International Conference of Systems, Man and Cybernetics, Atlanta, Ga., pp. 1621-1625.
Caffery, B., “Fractal Compression Breakthrough for Multimedia Applications”, Inside, Oct. 9, 1991.
Carpenter, G. A., S. Grossberg, “The Art of Adaptive Pattern Recognition by a Self-Organizing Neural Network”, IEEE Computer, March 1988, pp. 77-88.
Casasent, D., et al., “General I and Q Data Processing on a Multichannel AO System”, Applied Optics, 25(18):3217-24 (Sep. 15, 1986).
Caudill, M., “Neural Networks Primer-Part III”, AI Expert, June 1988, pp. 53-59.
Chao, J. J., E. Drakopoulos, C. C. Lee, “An evidential reasoning approach to distributed multiple hypothesis detection”, Proceedings of the 20th Conference on decision and control, Los Angeles, Calif., December 1987.
Chao, T.-H.; Hegblom, E.; Lau, B.; Stoner, W. W.; Miceli, W. J., “Optoelectronically implemented neural network with a wavelet preprocessor”, Proceedings of the SPIE-The International Society for Optical Engineering, 2026:472-82 (1993).
Chen et al., “Adaptive Coding of Monochrome and Color Images”, November 1977, pp. 1285-1292.
Cheong, C. K.; Aizawa, K.; Saito, T.; Hatori, M., “Adaptive edge detection with fractal dimension”, Transactions of the Institute of Electronics, Information and Communication Engineers D-II, J76D-II(11):2459-63 (1993).
Computer Visions, Graphics, and Image Processing, 1987, 37:54-115.
Computers and Biomedical Research 5, 388-410 (1972).
Cooper, L. N., “A Possible Organization of Animal Memory and Learning”, Nobel 24, (1973), Collective Properties of Physical Systems, pp. 252-264.
Crawford et al., “Adaptive Pattern Recognition Applied To An Expert System For Fault Diagnosis In Telecommunications Equipment”, pp. 10/1-8 (Inspec. Abstract No. 86C010699, Inspec IEE (London) & IEE Coll. on “Adaptive Filters”, Digest No. 76, Oct. 10, 1985).
Danielsson, Erik, et al., “Computer Architectures for Pictorial Inf. Systems”, IEEE Computer, November, 1981, pp. 53-67.
Dempster, A. P., “A generalization of Bayesian inference”, Journal of the Royal Statistical Society, Vol. 30, Series B (1968).
Dempster, A. P., “Upper and lower probabilities induced by a multivalued mapping”, Annals of mathematical Statistics, no. 38 (1967).
Denker, 1984 International Test Conf., October 1984, Philadelphia, Pa., pp. 558-563.
Dubois, D., “Modeles mathematiques de l'imprecis et de l'incertain en vue d'applications aux techniques d'aide a la decision”, Doctoral Thesis, University of Grenoble (1983).
Dubois, D., N. Prade, “Combination of uncertainty with belief functions: a reexamination”, Proceedings 9th International Joint Conference on Artificial Intelligence, Los Angeles (1985).
Dubois, D., N. Prade, “Fuzzy sets and systems-Theory and applications”, Academic Press, New York (1980).
Dubois, D., N. Prade, “Theorie des possibilites: application ala representation des connaissances en informatique”, Masson, Paris (1985).
Duda, R. O., P. E. Hart, M. J. Nilsson, “Subjective Bayesian methods for rule-based inference systems”, Technical Note 124, Artificial Intelligence Center, SRI International.
Dunning, B. B., “Self-Learning Data-Base For Automated Fault Localization”, IEEE, 1979, pp. 155-157.
Farrelle, Paul M. and Jain, Anil K., “Recursive Block Coding—A New Approach to Transform Coding”, IEEE Transactions on Communications, Com. 34(2) (Feb. 1986).
Fitzpatrick, J. M., J. J. Grefenstette, D. Van Gucht, “Image Registration by Genetic Search”, Conf. Proc., IEEE Southeastcon 1984, pp. 460-464.
Fua, P. V., “Using probability density functions in the framework of evidential reasoning Uncertainty in knowledge based systems”, B. Bouchon, R. R. Yager, Eds. Springer Verlag (1987).
Gogoussis et al., Proc. SPIE Intl. Soc. Opt. Eng., Nov. 1984, Cambridge, Mass., pp. 121-127.
Grossberg, S., G. Carpenter, “A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine”, Computer Vision, Graphics, and Image Processing, 1987, 37, 54-115, 252-315.
Gullichsen, E., E. Chang, “Pattern Classification by Neural Network: An Experiment System for Icon Recognition”, ICNN Proceeding on Neural Networks, March 1987, pp. IV-725-32.
Haruki, K. et al., “Pattern Recognition of Handwritten Phonetic Japanese Alphabet Characters”, International Joint Conference on Neural Networks, Washington, D.C., January 1990, pp. II-515 to II-518.
Hayashi, Y., et al., “Alphanumeric Character Recognition Using a Connectionist Model with the Pocket Algorithm”, Proceedings of the International Joint Conference on Neural Networks, Washington, D.C. Jun. 18-22, 1989, vol. 2, pp. 606-613.
Hayes, H. I.; Solka, J. L.; Priebe, C. E.; “Parallel computation of fractal dimension”, Proceedings of the SPIE-The International Society for Optical Engineering, 1962:219-30 (1993).
Hinton et al., “Boltzmann Machines: Constraint Satisfaction Networks that Learn”, Tech. Report CMU-CS-85-119, Carnegie-Mellon Univ, 5/84.
Hoare, F.; de Jager, G., “Neural networks for extracting features of objects in images as a pre-processing stage to pattern classification”, Proceedings of the 1992 South African Symposium on Communications and Signal Processing. COMSIG '92 (Cat. No. 92TH0482-0). Inggs, M. (Ed.), p. 239-42 (1992).
Hopfield et al., “Computing with Neural Circuits: A Model”, Science, 233:625-633 (8 Aug. 1986).
Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities”, Proc. Natl. Acad. Sci. USA, 79:2554-2558 (April 1982).
Hopfield, “Neurons with graded response have collective computational properties like those of two-state neurons”, Proc. Natl. Acad. Sci. USA, 81:3088-3092 (May 1984).
Hurtgen, B.; Buttgen, P., “Fractal approach to low rate video coding”, Proceedings of the SPIE-The International Society for Optical Engineering, 2094(pt.1):120-31 (1993).
Information Processing 71, North-Holland Publishing Company (1972) pp. 1530-1533.
Ishizuka, M., “Inference methods based on extended Dempster and Shafer's theory for problems with uncertainty/fuzziness”, New Generation Computing, Ohmsha, Ltd, and Springer Verlag, 1:159-168 (1983).
Jackel, L. D., H. P. Graf, J. S. Denker, D. Henderson and I. Guyon, “An Application of Neural Net Chips: Handwritten Digit Recognition”, ICNN Proceeding, 1988, pp. II-107-15.
Jean, J. S. N., et al., “Input Representation and Output Voting Considerations for Handwritten Numeral Recognition with Backpropagation”, International Joint Conference on Neural Networks, Washington, D.C., Jan. 1990, pp. I-408 to 1-411.
Jeffrey, R. J., “The logic of decision”, The University of Chicago Press, Ltd., London (1983)(2nd Ed.).
Kaufmann, A., “Introduction a la theorie des sous-ensembles flous”, Vol. 1, 2 et 3, Masson, Paris (1975).
Keeney, R. L., B. Raiffa, “Decisions with multiple objectives: Preferences and value tradeoffs”, John Wiley and Sons, New York (1976).
Kellman, P., “Time Integrating Optical Signal Processing”, Ph.D. Dissertation, Stanford University, 1979, pp. 51-55.
Kim, D. H.; Caulfield, H. J.; Jannson, T.; Kostrzewski, A.; Savant, G, “Optical fractal image processor for noise-embedded targets detection”, Proceedings of the SPIE-The International Society for Optical Engineering, Vol: 2026 p. 144-9 (1993) (SPIE Conf: Photonics for Processors, Neural Networks, and Memories 12-15 Jul. 1993, San Diego, Calif., USA).
Kohonen, “Self-Organization & Memory”, Second Ed., 1988, Springer-Verlag, pp. 199-209.
Kortegaard, B. L., “PAC-MAN, a Precision Alignment Control System for Multiple Laser Beams Self-Adaptive Through the Use of Noise”, Los Alamos National Laboratory, date unknown.
Kortegaard, B. L., “Superfine Laser Position Control Using Statistically Enhanced Resolution in Real Time”, Los Alamos National Laboratory, SPIE-Los Angeles Technical Symposium, Jan. 23-25, 1985.
Ksienski et al., “Low Frequency Approach to Target Identification”, Proc. of the IEEE, 63(12):1651-1660 (Dec. 1975).
Kyburg, H. E., “Bayesian and non Bayesian evidential updating”, Artificial Intelligence 31:271-293 (1987).
LeCun, Y. et al., “Handwritten Digit Recognition: Applications of Neural.”, IEEE Comm. Magazine, November 1989, pp. 41-46.
LeCun, Y., “Connectionism in Perspective”, in R. Pfeifer, Z. Schreter, F. Fogelman, L. Steels (Eds.), 1989, “Generalization and Network Design Strategies”, pp. 143-155.
Liepins, G. E., M. R. Hilliard, “Genetic Algorithms: Foundations &Applications”, Annals of Operations Research, 21:31-58 (1989).
Lin, H. K., et al., “Real-Time Screen-Aided Multiple-Image Optical Holographic Matched-Filter Correlator”, Applied Optics, 21(18):3278-3286 (Sep. 15, 1982).
Lippman, R. P., “An Introduction to Computing with Neural Nets”, IEEE ASSP Magazine, April 1987, pp. 4-22.
Lippmann, R. P., “An Introduction to Computing with Neural Nets”, IEEE ASSP Magazine, vol. 4(2):4-22 (April 1987).
Liu, Y., “Extensions of fractal theory”, Proceedings of the SPIE-The International Society for Optical Engineering, 1966:255-68 (1993).
Liu, Y., “Pattern recognition using Hilbert space”, Proceedings of the SPIE-The International Society for Optical Engineering, 1825:63-77 (1992).
Mahalanobis, A., et al., “Minimum Average Correlation Energy Filters”, Applied Optics, 26(17):3633-40 (Sep. 1, 1987).
Martin, G. L. et al., “Recognizing Hand-Printed Letters and Digits Using Backpropagation Learning”, Technical Report of the MCC, Human Interface Laboratory, Austin, Tex., Jan. 1990, pp. 1-9.
McAulay, A. D., J. C. Oh, “Image Learning Classifier System Using Genetic Algorithms”, IEEE Proc. of the National Aerospace & Electronics Conference, 2:705-710 (1989).
Miller, R. K., Neural Networks ((c) 1989: Fairmont Press, Lilburn, Ga.), pp. 2-12 and Chapter4, “Implementation of Neural Networks”, pp. 4-1 to 4-26.
Molley, P., “Implementing the Difference-Squared Error Algorithm Using An Acousto-Optic Processor”, SPIE, 1098:232-239 (1989).
Molley, P., et al., “A High Dynamic Range Acousto-Optic Image Correlator for Real-Time Pattern Recognition”, SPIE, 938:55-65 (1988).
Mori, “Towards the construction of a large-scale neural network”, Electronics Information Communications Association Bulletin PRU 88-59, pp. 87-94.
Naik et al., “High Performance Speaker Verification.”, ICASSP 86, Tokyo, CH2243-4/86/0000-0881, IEEE 1986, pp. 881-884.
Ney, H., et al., “A Data Driven Organization of the Dynamic Programming Beam Search for Continuous Speech Recognition”, Proc. ICASSP 87, pp. 833-836,1987.
Nilsson, N. J., The Mathematical Foundations of Learning Machines ((c) 1990: Morgan Kaufmann Publishers, San Mateo, Calif.) and particularly section 2.6 “The Threshold Logic Unit (TLU)”, pp. 21-23 and Chapter 6, “Layered Machines” pp. 95-114.
Ohsuga et al., “Entrainment of Two Coupled van der Pol Oscillators by an External Oscillation”, Biological Cybernetics, 51:225-239 (1985).
Omata et al., “Holonic Model of Motion Perception”, IEICE Technical Reports, Mar. 26, 1988, pp. 339-346.
O'Neal et al., “Coding Isotropic Images”, Nov. 1977, pp. 697-707.
Pawlicki, T. F., D. S. Lee, J. J. Hull and S. N. Srihari, “Neural Network Models and their Application to Handwritten Digit Recognition”, ICNN Proceeding, 1988, pp. II-63-70.
Perry et al., “Auto-Indexing Storage Device”, IBM Tech. Disc. Bulletin, 12(8): 1219 (January 1970).
Peterson, Ivars, “Packing It In”, Science News, 131(18):283-285 (May 2, 1987).
Priebe, C. E.; Solka, J. L.; Rogers, G. W., “Discriminant analysis in aerial images using fractal based features”, Proceedings of the SPIE-The International Society for Optical Engineering, 1962:196-208 (1993).
Proceedings, 6th International Conference on Pattern Recognition 1982, pp. 152-136.
Psaltis, D., “Incoherent Electro-Optic Image Correlator”, Optical Engineering, 23(1):12-15 (January/February 1984).
Psaltis, D., “Two-Dimensional Optical Processing Using One-Dimensional Input Devices”, Proceedings of the IEEE, 72(7):962-974 (July 1984).
Rahmati, M.; Hassebrook, L. G., “Intensity- and distortion-invariant pattern recognition with complex linear morphology”, Pattern Recognition, 27 (4):549-68 (1994).
Reusens, E., “Sequence coding based on the fractal theory of iterated transformations systems”, Proceedings of the SPIE-The International Society for Optical Engineering, 2094(pt.1):132-40 (1993).
Rhodes, W., “Acousto-Optic Signal Processing: Convolution and Correlation”, Proc. of the IEEE, 69(1):65-79 (Jan. 1981).
Rosenfeld, Azriel and Avinash C. Kak, Digital Picture Processing, Second Edition, Volume 2, Academic Press, 1982.
Roy, B., “Classements et choix en presence de points de vue multiples”, R.I.R.O.-2eme annee-no. 8, pp. 57-75 (1968).
Roy, B., “Electre III: un algorithme de classements fonde sur une representation floue des preferences en presence de criteres multiples”, Cahiers du CERO, 20(1):3-24 (1978).
Rumelhart, D. E., et al., “Learning Internal Representations by Error Propagation”, Parallel Distr. Proc.: Explorations in Microstructure of Cognition, 1:318-362 (1986).
Rumelhart, D. E., et al., Parallel Distributed Processing, ((c) 1986: MIT Press, Cambridge, Mass.), and specifically Chapter 8 thereof, “Learning Internal Representations by Error Propagation”, pp. 318-362.
Rutherford, H. G., F. Taub and B. Williams, “Object Identification and Measurement from Images with Access to the Database to Select Specific Subpopulations of Special Interest”, May 1986.
Rutter et al., “The Timed Lattice—A New Approach To Fast Converging Equalizer Design”, pp. VIII/1-5 (Inspec. Abstract No. 84C044315, Inspec IEE (London) & IEE Saraga Colloquium on Electronic Filters, May 21, 1984).
Sadjadi, F., “Experiments in the use of fractal in computer pattern recognition”, Proceedings of the SPIE-The International Society for Optical Engineering, 1960:214-22 (1993).
Sakoe, H., “A Generalization of Dynamic Programming Based Pattern Matching Algorithm Stack DP-Matching”, Transactions of the Committee on Speech Research, The Acoustic Society of Japan, p.S83-23, 1983.
Sakoe, H., “A Generalized Two-Level DP-Matching Algorithm for Continuous Speech Recognition”, Transactions of the IECE of Japan, E65(11):649-656 (November 1982).
Scharlic, A., “Decider sur plusieurs criteres. Panorama de l'aide ala decision multicritere”, Presses Polytechniques Romandes (1985).
Schurmann, J., “ZurZeichen and Worterkennung beim Automatischen Anschriftenlesen”, Wissenschaftlichl, Berichte, 52(1/2) (1979).
Scientific American, “Not Just a Pretty Face”, March 1990, pp. 77-78.
Shafer, G., “A mathematical theory of evidence”, Princeton University Press, Princeton, N.J. (1976).
Shimizu et al., “Principle of Holonic Computer and Holovision”, Journal of the Institute of Electronics, Information and Communication, 70(9):921-930 (1987).
Shinan et al., “The Effects of Voice Disguise.”, ICASSP 86, Tokyo, CH2243-4/86/0000-0885, IEEE 1986, pp. 885-888.
Silverston et al., “Spectral Feature Classification and Spatial Pattern Rec.”, SPIE 201:17-26, Optical Pattern Recognition (1979).
Simpson, W. R., C. S. Dowling, “WRAPLE: The Weighted Repair Assistance Program Learning Extension”, IEEE Design & Test, 2:66-73 (April 1986).
Specht, IEEE Internatl. Conf. Neural Networks, 1:1525-1532 (July 1988), San Diego, Calif.
Sprageu, R. A., “A Review of Acousto-Optic Signal Correlators”, Optical Engineering, 16(5):467-74 (September/October 1977).
Sprinzak, J.; Werman, M., “Affine point matching”, Pattern Recognition Letters, 15(4):337-9 (1994).
Stanley R. Sternberg, “Biomedical Image Processing”, IEEE Computer, 1983, pp. 22-34.
Stewart, R. M., “Expert Systems For Mechanical Fault Diagnosis”, IEEE, 1985, pp. 295-300.
Sugeno, M., “Theory of fuzzy integrals and its applications”, Tokyo Institute of Technology (1974).
Svetkoff et al., Hybrid Circuits (GB), No. 13, May 1987, pp. 5-8.
Udagawa, K., et al, “A Parallel Two-Stage Decision Method for Statistical Character Recognition.”, Electronics and Communications in Japan (1965).
Vander Lugt, A., “Practical Considerations for the Use of Spatial Carrier-Frequency Filters”, Applied Optics, 5(11):1760-1765 (November 1966).
Vander Lugt, A., “Signal Detection By Complex Spatial Filtering”, IEEE Transactions On Information Theory, IT-10, 2:139-145 (April 1964).
Vander Lugt, A., et al., “The Use of Film Nonlinearites in Optical Spatial Filtering”, Applied Optics, 9(1):215-222 (Jan. 1970).
Vannicola et al., “Applications of Knowledge Based Systems to Surveillance”, Proceedings of the 1988 IEEE National Radar Conference, 20-21 Apr. 1988, pp. 157-164.
Vitols, “Hologram Memory for Storing Digital Data”, IBM Tech. Disc. Bulletin 8(11):1581-1583 (April 1966).
Wald, Sequential Analysis, Dover Publications Inc., 1947, pp. 34-43.
Wasserman, Philip D., “Neural Computing-Theory & Practice”, 1989, pp. 128-129.
Willshaw et al., “Non-Holographic Associative Memory”, Nature, 222:960-962 (Jun. 7, 1969).
Yager, R. R., “Entropy and specificity in a mathematical theory of Evidence”, Int. J. General Systems, 9:249-260 (1983).
Yamada et. al., “Character recognition system using a neural network”, Electronics Information Communications Association Bulletin PRU 88-58, pp. 79-86.
Yamane et al., “An Image Data Compression Method Using Two-Dimensional Extrapolative Prediction-Discrete Sine Transform”, Oct. 29-31, 1986, pp. 311-316.
Zadeh, L. A., “Fuzzy sets as a basis for a theory of possibility”, Fuzzy sets and Systems, 1:3-28 (1978).
Zadeh, L. A., “Fuzzy sets”, Information and Control, 8:338-353 (1965).
Zadeh, L. A., “Probability measures of fuzzy events”, Journal of Mathematical Analysis and Applications, 23:421-427 (1968).
Zhi-Yan Xie; Brady, M., “Fractal dimension image for texture segmentation”, ICARCV '92. Second International Conference on Automation, Robotics and Computer Vision, p. CV-4.3/1-5 vol. 1, (1992).
Zhu, X., et al., “Feature Detector and Application to Handwritten Character Recognition”, International Joint Conference on Neural Networks, Washington, D.C., January 1990, pp. II-457 to II-460.
The above-mentioned references are exemplary, and are not meant to be limiting in respect to the resources and/or technologies available to those skilled in the art. Of course it should be realized that the hardware for implementing a system may be integrally related to the choice of specific method or software algorithm for implementing the system, and therefore these together form a system. It is noted that in view of the present disclosure, it is within the skill of the artisan to combine in various fashions the available methods and apparatus to achieve the advanced interface and control system of the present invention.